Usher Syndrome miRNA Microarray Analysis
Wesley A. Tom
2024-01-16
Some Useful Packages
#If you haven't installed them here you go:
#install.packages("NACHO")
#install.packages('nanostringr')
library(BiocManager)
#BiocManager::install("GEOquery")
#BiocManager::install("limma")
#load them up:
library(NACHO)
library(nanostringr)
library(ggsci)
library(ggplot2)
library(gridExtra)
library(limma)
library(data.table)
library(png)
library(dplyr)
library(phyloseq)
library(DESeq2)
library(ComplexHeatmap)
pal1 <- pal_simpsons("springfield")(16) #makes a color palette for the figuresData Import and Normalization
Here we read in the .RCC count files for all of the miRNA microarray assays in the lab currently. We will subset this later to just Usher and NBT cell lines. Here we use the NACHO package to normalize the data. The top houskeeping miRNAs are predicted by NACHO (housekeeping_predict = T) using the function:
find_housekeeping <- function(data, id_colname, count_column) {
CodeClass <- NULL # no visible binding for global variable
data <- data[grep("Endogenous|Housekeeping", CodeClass)]
ratios <- lapply(
X = split(data, data[[id_colname]]),
count_column = count_column,
FUN = function(.data, count_column) {
.data <- .data[j = .SD, .SDcols = c("Name", count_column)]
sample_means <- mean(.data[[count_column]])
ratios <- log2(.data[[count_column]] / sample_means)
names(ratios) <- .data[["Name"]]
ratios[is.infinite(ratios)] <- NA_real_
ratios
}
)
ratios <- do.call("cbind", ratios)
ratiosd <- apply(X = ratios, MARGIN = 1, FUN = stats::sd, na.rm = TRUE)
ratiosd <- sort(ratiosd, decreasing = FALSE)
names(ratiosd)[1:5]
}and the count data are normalized by their respective geometric mean (normalisation_method = “GEO”) using the following:
geometric_mean <- function(x) {
x[x == 0] <- 1
exp(mean(log(x)))
}“The normalisation methods GEOor GLM are used to compute the normalisation factors for positive and housekeeping probes. They also used to compute the”negative factor” (poor naming choice though): “geometric mean” (GEO) or the corresponding value from GLM of negative probes.
To summarise NACHO computes a data.frame with three columns:
“Positive_factor” “Negative_factor” “House_factor” (not necessarily used) The simple formula for the normalisation is: (Count_i - geo(Negative_i)) * PositiveF (with PositiveF: mean(geo(Positive)) / geo(Positive_i)) or (Count - Negative) * PositiveF * HousekeepingF In addition negative counts are replaced with 0.
For short, background is taken into account. Although, you can add additional threshold to be more “biologically relevant”.
If you are interested in details:
R/norm_geo.R R/norm_glm.R geometric_housekeeping.R factor_calculation.R normalise_counts.R”
ush_mirna_nacho <-load_rcc(data_directory = "rcc_files/",
ssheet_csv = "samplesheet.csv",
id_colname = "IDFILE",
housekeeping_predict = T)## [NACHO] Importing RCC files.## [NACHO] Performing QC and formatting data.## [NACHO] Searching for the best housekeeping genes.## [NACHO] Computing normalisation factors using "GEO" method for housekeeping genes prediction.## [NACHO] The following predicted housekeeping genes will be used for normalisation:
## - hsa-miR-664b-5p
## - hsa-miR-887-3p
## - hsa-miR-29c-3p
## - hsa-miR-373-3p
## - hsa-miR-3150b-3p## [NACHO] Computing normalisation factors using "GEO" method.## [NACHO] Normalising data using "GEO" method with housekeeping genes.## [NACHO] Returning a list.
## $ access : character
## $ housekeeping_genes : character
## $ housekeeping_predict: logical
## $ housekeeping_norm : logical
## $ normalisation_method: character
## $ remove_outliers : logical
## $ n_comp : numeric
## $ data_directory : character
## $ pc_sum : data.frame
## $ nacho : data.frame
## $ outliers_thresholds : listush_norm <- normalise(
nacho_object = ush_mirna_nacho,#the imported data
#housekeeping_genes = housekeeping, #can provide a list of housekeeping genes if preferred
housekeeping_predict = T, #predict good housekeeping candidates
housekeeping_norm = TRUE, #normalize relative to predicted housekeeping genes
normalisation_method = "GEO", #geometric mean calculation
remove_outliers = F #you can specify whether to keep or get rid of outliers that did not make the QC cutoff for one reason or another...
)## [NACHO] Nothing was done. Parameters in "normalise()", were the same as in "ush_mirna_nacho".#visualise(ush_norm)
#render(ush_norm)Extract the data that we want to look at, in this case the sample data and the count data:
#Select phenotype data that you want, in our case this takes the columns from our sample data (SD)
#that match what we want to look at, and makes a data.table object of them calls selected_pheno:
selected_pheno <- ush_norm[["nacho"]][
j = lapply(unique(.SD), function(x) ifelse(x == "NA", NA, x)),
.SDcols = c("IDFILE", "sampleID", "cartridgeID", "phenotype","genotype","source","USHvsCONT","Immortal_vs_fresh","carrier_vs_nbt","cell_source","cell_status","origin","study")
]
selected_pheno <- na.exclude(selected_pheno) #remove any data from the data table that has n/a or empty information
#get normalised cout data from our "nacho" object
expr_counts <- ush_norm[["nacho"]][
i = grepl("Endogenous", CodeClass),
j = as.matrix(
dcast(.SD, Name ~ IDFILE, value.var = "Count_Norm"),
"Name"
),
.SDcols = c("IDFILE", "Name", "Count_Norm")
]
samplekept <- intersect(selected_pheno[["IDFILE"]], colnames(expr_counts))
expr_counts <- expr_counts[,samplekept]
selected_pheno <- selected_pheno[IDFILE %in% c(samplekept)]
#write.table(expr_counts, file = "all_miRNA_array.norm.epr.cts.tsv", sep="\t")Now is the point where we will subset the dataset into the samples of interest, which are the Usher phenotypes and our NBT control phenotypes transformed with EBV.
#subset dataset to match comparisons desired....
#we need last 2 runs Usher samples plus controls -NBTi from newest run...
#could reach out to Jennifer at UNMC and see how she goes about combining runs...
ush_vs_cont_pheno <- selected_pheno[selected_pheno$USHvsCONT %in% c("yes"),]
ush_vs_cont_samplekept <- intersect(ush_vs_cont_pheno[["IDFILE"]], colnames(expr_counts))
ush_vs_cont_expr_counts <- expr_counts[,ush_vs_cont_samplekept]
rownames(ush_vs_cont_pheno) <- ush_vs_cont_pheno$sampleID
colnames(ush_vs_cont_expr_counts) <- ush_vs_cont_pheno$sampleID
#make some phyloseq objects
uvc_tax <- data.frame(ush_vs_cont_expr_counts)
uvc_tax$Kingdom <- paste0(rownames(uvc_tax))
uvc_tax$Phylum <- paste0(rownames(uvc_tax))
uvc_tax$Class <- paste0(rownames(uvc_tax))
uvc_tax$Order <-paste0(rownames(uvc_tax))
uvc_tax$Family <- paste0(rownames(uvc_tax))
uvc_tax$Genus <- paste0(rownames(uvc_tax))
uvc_tax$Species <- paste0(rownames(uvc_tax))
uvc_tax2 <- uvc_tax[,c(18:24)]
uvc_tax2 <- tax_table(as(uvc_tax2,"matrix"))
uvc.otu <- otu_table(ush_vs_cont_expr_counts,taxa_are_rows = T)
uvc.samdat<- sample_data(ush_vs_cont_pheno[,c(2,1,3:13)])
rownames(uvc.samdat) <- uvc.samdat$sampleID
uvc.ps <- phyloseq(uvc.otu,uvc.samdat,uvc_tax2)
uvc.ps## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 798 taxa and 17 samples ]
## sample_data() Sample Data: [ 17 samples by 13 sample variables ]
## tax_table() Taxonomy Table: [ 798 taxa by 7 taxonomic ranks ]PERMANOVA Analysis
This test will tell us if our phenotype miRNA profiles are significantly different from one another, here we use the variable sample type.
library(vegan)
set.seed(1234)
metadat <- data.frame(sample_data(uvc.ps))
permanova <- adonis2(phyloseq::distance(uvc.ps, method="bray") ~ phenotype + source, data= metadat, permutation=999)
permanova## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = phyloseq::distance(uvc.ps, method = "bray") ~ phenotype + source, data = metadat, permutations = 999)
## Df SumOfSqs R2 F Pr(>F)
## phenotype 3 0.66613 0.82682 20.688 0.001 ***
## Residual 13 0.13953 0.17318
## Total 16 0.80565 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1permanova$Model <- row.names(permanova)
permanova <-permanova[,c(6,1:5)]library(microViz)
library(ggrepel)
library(gridExtra)
# first we make a function that replaces any unwanted "_" in our taxa labels with spaces
library(stringr)
renamer <- function(x) str_replace(x, pattern = "hsa-miR-", replacement = "")
pca <- uvc.ps %>% microViz::ord_calc(method = "PCA")
pca_ord <- ord_plot(data = pca,
color = "phenotype",
shape = "phenotype",
plot_taxa = 1:8,
taxon_renamer = renamer,
tax_vec_length = 1.5, # this value is a scalar multiplier for the biplot score vectors
tax_lab_style = tax_lab_style(size = 2,
alpha = 0.8,
type = "text",
max_angle = 90,
check_overlap = T), # create a list of options to tweak the taxa labels' default style
constraint_vec_style = vec_constraint(size = 0.5, alpha = 0.75), # this styles the constraint arrows
constraint_lab_style = constraint_lab_style(size = 1, justify = "auto"),
size = 4) + # this styles the constrain
scale_color_manual(values = pal1) +
coord_fixed(ratio = 1, clip = "off", xlim = c(-400,500),ylim = c(-200,450))+
scale_shape_manual(name = "Cell Line Sample Type",
#breaks = c("NBT_A","USHtype1","USHtype2","USHtype3"),
labels = c("Control","USH1", "USH2","USH3"),
values = c(19,17,17,17)) +
scale_color_manual(name = "Cell Line Sample Type",
#breaks = c("NBT_A","USHtype1","USHtype2","USHtype3"),
labels = c("Control","USH1", "USH2","USH3"),
values = c("#FED439FF", "#709AE1FF", "#8A9197FF", "#91331FFF")) +
theme(plot.title = element_text(hjust = 0.5),
panel.border = element_rect(colour ="gray", fill = NA, size = .5),
plot.background = element_rect(colour = "gray", fill = NA, size = 2))
pca_ord
tt1 <- ttheme_minimal(core=list(bg_params = list(fill = "white",col=NA),
fg_params=list(fontface=3, fontsize=6)),
colhead=list(fg_params=list(fontface=4L, fontsize = 6),
bg_params = list(fill = "white")))
perm <- tableGrob(permanova, rows=NULL, theme = tt1)
library(gtable)
library(grid)
perm <- gtable_add_grob(perm,
grobs = rectGrob(gp = gpar(fill = NA, lwd = 2)),
t = 2, b = nrow(perm), l = 1, r = ncol(perm))
perm <- gtable_add_grob(perm,
grobs = rectGrob(gp = gpar(fill = NA, lwd = 2)),
t = 1, l = 1, r = ncol(perm))
pca_ord_perm <- pca_ord + annotation_custom(perm,
xmin=-450, xmax=100, ymin=250, ymax=500)
pca_ord_perm
#ggsave(filename = "PCA_permanova_usher_miRNA.pdf",pca_ord_perm,device = "pdf", width = 8, height = 5.5)DESeq2 diff miRNAs by phenotype:
library("DESeq2")#;packageVersion('DESeq2')
gm_mean = function(x, na.rm=TRUE){
exp(sum(log(x[x > 0]), na.rm=na.rm) / length(x))
}
dds <- phyloseq_to_deseq2(uvc.ps, ~phenotype)## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factorsgeoMeans <- apply(counts(dds), 1, gm_mean)
dds = estimateSizeFactors(dds,geoMeans=geoMeans)
dds = DESeq2::DESeq(dds, test = "Wald", fitType = "local")## using pre-existing size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingresultsNames(dds)## [1] "Intercept" "phenotype_USHtype1_vs_NBT_A"
## [3] "phenotype_USHtype2_vs_NBT_A" "phenotype_USHtype3_vs_NBT_A"#########################################################################################################################
ush1.nbt.res = results(dds, contrast =c("phenotype","USHtype1","NBT_A") )
ush1.nbt.res = ush1.nbt.res[order(ush1.nbt.res$padj, na.last=NA), ]
alpha = 0.05
ush1.nbt.sigtab = ush1.nbt.res[(ush1.nbt.res$padj < alpha), ]
ush1.nbt.sigtab = cbind(as(ush1.nbt.sigtab, "data.frame"), as(tax_table(uvc.ps)[rownames(ush1.nbt.sigtab), ], "matrix"))
#order df by log fold change and manually inspect where 2fold change threshold is, in this case row 76:
o = order(abs(ush1.nbt.sigtab$log2FoldChange), decreasing = TRUE)
ush1.nbt.sigtab2 = ush1.nbt.sigtab[o, ]
ush1.nbt.sigtab2 = ush1.nbt.sigtab2[1:63,]
ush1.nbt.sigtab2## baseMean log2FoldChange lfcSE
## hsa-miR-222-3p 62.352476 8.976366 1.0862126
## hsa-miR-151a-5p 12.943991 -7.652711 1.4140075
## hsa-miR-424-5p 26.056195 7.140241 1.5007809
## hsa-miR-503-5p 22.185375 6.870487 1.0943090
## hsa-miR-129-2-3p 6.247654 -6.585901 1.8476038
## hsa-miR-1252-5p 14.162051 6.541487 1.1172016
## hsa-miR-1246 1161.454061 -6.441675 0.4618959
## hsa-miR-182-5p 13.386544 -6.269056 1.7802903
## hsa-miR-1205 11.550695 6.250660 1.1192896
## hsa-miR-450a-5p 22.279584 5.927644 1.5799898
## hsa-miR-654-5p 5.891644 5.915866 1.2486557
## hsa-miR-519d-3p 3.246283 5.575579 1.6558303
## hsa-miR-4431 2.400438 5.438074 1.9621137
## hsa-miR-345-5p 5.074769 -5.414299 1.3203787
## hsa-miR-3934-5p 5.185416 5.396254 1.2974910
## hsa-miR-221-5p 4.368518 5.309392 1.6473012
## hsa-miR-152-3p 3.702954 -5.218438 1.4915337
## hsa-miR-1260b 2.641261 4.969649 1.7505240
## hsa-miR-34a-5p 454.895501 4.897727 0.4509239
## hsa-miR-150-5p 452.972299 4.890114 0.6106738
## hsa-let-7d-5p 588.882373 4.811113 0.3670308
## hsa-miR-483-3p 7.946179 4.715437 1.1509874
## hsa-miR-146b-5p 168.632116 4.652693 0.4681016
## hsa-miR-371b-5p 3.811814 4.622638 1.3699610
## hsa-miR-601 6.414515 -4.573882 1.2045490
## hsa-miR-5010-3p 3.921794 4.453129 1.4213647
## hsa-miR-591 6.943709 4.275723 1.2521012
## hsa-miR-155-5p 11097.869650 4.149979 0.2869964
## hsa-miR-363-3p 319.076852 4.128234 0.3272720
## hsa-miR-3916 4.334913 -3.864626 1.2674039
## hsa-let-7i-5p 1348.149768 3.814222 0.3545566
## hsa-miR-1827 6.679916 3.800521 1.0123302
## hsa-miR-4455 14.743625 -3.777094 1.1651531
## hsa-miR-3195 3.248441 -3.771340 1.4107073
## hsa-miR-195-5p 2.898308 -3.751481 1.5077286
## hsa-miR-1226-3p 4.558990 3.710526 1.3886886
## hsa-miR-27a-3p 3.104900 3.700705 1.5096094
## hsa-miR-194-5p 42.517210 -3.379095 0.4028265
## hsa-miR-28-3p 59.451504 -3.358252 0.4224829
## hsa-miR-151a-3p 10.199230 -3.351741 0.7765824
## hsa-miR-10a-5p 44.111464 3.055764 0.4277549
## hsa-miR-146a-5p 4963.645408 2.989872 0.4023580
## hsa-miR-337-3p 54.247834 2.981863 0.4663584
## hsa-miR-200c-3p 16.731190 2.942878 0.7288186
## hsa-miR-96-5p 34.142701 -2.938594 0.3424560
## hsa-miR-5196-3p+hsa-miR-6732-3p 14.758482 2.777010 0.6499589
## hsa-miR-551b-3p 33.524252 2.734946 0.4275855
## hsa-miR-299-3p 27.522827 -2.675616 0.7050000
## hsa-let-7f-5p 763.539513 2.652028 0.3456879
## hsa-miR-577 4.625677 -2.605055 0.9970668
## hsa-miR-181b-2-3p 7.627180 2.530375 0.8285307
## hsa-let-7g-5p 2774.856795 2.442113 0.3122241
## hsa-miR-1287-5p 7.496055 2.357994 0.9396846
## hsa-miR-324-5p 48.937752 -2.290057 0.7169758
## hsa-miR-484 46.995584 -2.287798 0.3329308
## hsa-miR-183-5p 36.030068 -2.277099 0.3826565
## hsa-miR-582-5p 32.434430 -2.265008 0.6650003
## hsa-let-7b-5p 108.579356 2.203120 0.2818926
## hsa-miR-16-5p 9716.681454 -2.184471 0.3741371
## hsa-miR-331-3p 216.410641 -2.176946 0.3457068
## hsa-miR-148a-3p 543.177533 2.173471 0.3608197
## hsa-miR-98-5p 271.663474 2.170957 0.3540343
## hsa-miR-26b-5p 457.008546 -2.124356 0.3741439
## stat pvalue padj
## hsa-miR-222-3p 8.263913 1.409735e-16 6.653947e-15
## hsa-miR-151a-5p -5.412072 6.229962e-08 7.738269e-07
## hsa-miR-424-5p 4.757684 1.958272e-06 2.009357e-05
## hsa-miR-503-5p 6.278379 3.421215e-10 6.728389e-09
## hsa-miR-129-2-3p -3.564563 3.644624e-04 2.047931e-03
## hsa-miR-1252-5p 5.855243 4.763138e-09 7.494004e-08
## hsa-miR-1246 -13.946163 3.319940e-44 7.835059e-42
## hsa-miR-182-5p -3.521367 4.293277e-04 2.356310e-03
## hsa-miR-1205 5.584488 2.343891e-08 3.253873e-07
## hsa-miR-450a-5p 3.751698 1.756413e-04 1.105369e-03
## hsa-miR-654-5p 4.737788 2.160636e-06 2.124625e-05
## hsa-miR-519d-3p 3.367241 7.592437e-04 3.853366e-03
## hsa-miR-4431 2.771539 5.579206e-03 2.330430e-02
## hsa-miR-345-5p -4.100565 4.121427e-05 2.903453e-04
## hsa-miR-3934-5p 4.158991 3.196558e-05 2.357462e-04
## hsa-miR-221-5p 3.223085 1.268179e-03 6.107965e-03
## hsa-miR-152-3p -3.498706 4.675222e-04 2.536442e-03
## hsa-miR-1260b 2.838950 4.526230e-03 1.959982e-02
## hsa-miR-34a-5p 10.861538 1.757632e-27 1.659205e-25
## hsa-miR-150-5p 8.007735 1.168402e-15 5.013506e-14
## hsa-let-7d-5p 13.108201 2.955238e-39 4.649575e-37
## hsa-miR-483-3p 4.096863 4.187874e-05 2.906877e-04
## hsa-miR-146b-5p 9.939493 2.802494e-23 1.889682e-21
## hsa-miR-371b-5p 3.374285 7.400778e-04 3.796921e-03
## hsa-miR-601 -3.797174 1.463552e-04 9.671047e-04
## hsa-miR-5010-3p 3.132995 1.730323e-03 8.167125e-03
## hsa-miR-591 3.414838 6.381987e-04 3.346998e-03
## hsa-miR-155-5p 14.460040 2.166726e-47 1.022695e-44
## hsa-miR-363-3p 12.614077 1.766155e-36 2.084062e-34
## hsa-miR-3916 -3.049246 2.294166e-03 1.051307e-02
## hsa-let-7i-5p 10.757725 5.449920e-27 4.287270e-25
## hsa-miR-1827 3.754231 1.738747e-04 1.105369e-03
## hsa-miR-4455 -3.241715 1.188127e-03 5.877339e-03
## hsa-miR-3195 -2.673368 7.509375e-03 3.016351e-02
## hsa-miR-195-5p -2.488167 1.284034e-02 4.556872e-02
## hsa-miR-1226-3p 2.671964 7.540878e-03 3.016351e-02
## hsa-miR-27a-3p 2.451432 1.422889e-02 4.974842e-02
## hsa-miR-194-5p -8.388462 4.925435e-17 2.583117e-15
## hsa-miR-28-3p -7.948847 1.882553e-15 7.404710e-14
## hsa-miR-151a-3p -4.316015 1.588711e-05 1.249786e-04
## hsa-miR-10a-5p 7.143728 9.083316e-13 2.381847e-11
## hsa-miR-146a-5p 7.430874 1.078828e-13 3.182542e-12
## hsa-miR-337-3p 6.393931 1.616741e-10 3.468645e-09
## hsa-miR-200c-3p 4.037874 5.393782e-05 3.689660e-04
## hsa-miR-96-5p -8.580940 9.410125e-18 5.551974e-16
## hsa-miR-5196-3p+hsa-miR-6732-3p 4.272593 1.932130e-05 1.495025e-04
## hsa-miR-551b-3p 6.396254 1.592346e-10 3.468645e-09
## hsa-miR-299-3p -3.795200 1.475244e-04 9.671047e-04
## hsa-let-7f-5p 7.671740 1.696780e-14 5.339201e-13
## hsa-miR-577 -2.612718 8.982532e-03 3.505201e-02
## hsa-miR-181b-2-3p 3.054051 2.257739e-03 1.044758e-02
## hsa-let-7g-5p 7.821669 5.212766e-15 1.846314e-13
## hsa-miR-1287-5p 2.509346 1.209549e-02 4.358071e-02
## hsa-miR-324-5p -3.194051 1.402914e-03 6.688638e-03
## hsa-miR-484 -6.871692 6.344478e-12 1.576102e-10
## hsa-miR-183-5p -5.950765 2.668923e-09 4.343903e-08
## hsa-miR-582-5p -3.406025 6.591607e-04 3.418944e-03
## hsa-let-7b-5p 7.815458 5.476356e-15 1.846314e-13
## hsa-miR-16-5p -5.838691 5.261246e-09 8.010672e-08
## hsa-miR-331-3p -6.297089 3.032879e-10 6.223995e-09
## hsa-miR-148a-3p 6.023706 1.704680e-09 2.980033e-08
## hsa-miR-98-5p 6.132053 8.675229e-10 1.574888e-08
## hsa-miR-26b-5p -5.677913 1.363481e-08 1.950191e-07
## Kingdom
## hsa-miR-222-3p hsa-miR-222-3p
## hsa-miR-151a-5p hsa-miR-151a-5p
## hsa-miR-424-5p hsa-miR-424-5p
## hsa-miR-503-5p hsa-miR-503-5p
## hsa-miR-129-2-3p hsa-miR-129-2-3p
## hsa-miR-1252-5p hsa-miR-1252-5p
## hsa-miR-1246 hsa-miR-1246
## hsa-miR-182-5p hsa-miR-182-5p
## hsa-miR-1205 hsa-miR-1205
## hsa-miR-450a-5p hsa-miR-450a-5p
## hsa-miR-654-5p hsa-miR-654-5p
## hsa-miR-519d-3p hsa-miR-519d-3p
## hsa-miR-4431 hsa-miR-4431
## hsa-miR-345-5p hsa-miR-345-5p
## hsa-miR-3934-5p hsa-miR-3934-5p
## hsa-miR-221-5p hsa-miR-221-5p
## hsa-miR-152-3p hsa-miR-152-3p
## hsa-miR-1260b hsa-miR-1260b
## hsa-miR-34a-5p hsa-miR-34a-5p
## hsa-miR-150-5p hsa-miR-150-5p
## hsa-let-7d-5p hsa-let-7d-5p
## hsa-miR-483-3p hsa-miR-483-3p
## hsa-miR-146b-5p hsa-miR-146b-5p
## hsa-miR-371b-5p hsa-miR-371b-5p
## hsa-miR-601 hsa-miR-601
## hsa-miR-5010-3p hsa-miR-5010-3p
## hsa-miR-591 hsa-miR-591
## hsa-miR-155-5p hsa-miR-155-5p
## hsa-miR-363-3p hsa-miR-363-3p
## hsa-miR-3916 hsa-miR-3916
## hsa-let-7i-5p hsa-let-7i-5p
## hsa-miR-1827 hsa-miR-1827
## hsa-miR-4455 hsa-miR-4455
## hsa-miR-3195 hsa-miR-3195
## hsa-miR-195-5p hsa-miR-195-5p
## hsa-miR-1226-3p hsa-miR-1226-3p
## hsa-miR-27a-3p hsa-miR-27a-3p
## hsa-miR-194-5p hsa-miR-194-5p
## hsa-miR-28-3p hsa-miR-28-3p
## hsa-miR-151a-3p hsa-miR-151a-3p
## hsa-miR-10a-5p hsa-miR-10a-5p
## hsa-miR-146a-5p hsa-miR-146a-5p
## hsa-miR-337-3p hsa-miR-337-3p
## hsa-miR-200c-3p hsa-miR-200c-3p
## hsa-miR-96-5p hsa-miR-96-5p
## hsa-miR-5196-3p+hsa-miR-6732-3p hsa-miR-5196-3p+hsa-miR-6732-3p
## hsa-miR-551b-3p hsa-miR-551b-3p
## hsa-miR-299-3p hsa-miR-299-3p
## hsa-let-7f-5p hsa-let-7f-5p
## hsa-miR-577 hsa-miR-577
## hsa-miR-181b-2-3p hsa-miR-181b-2-3p
## hsa-let-7g-5p hsa-let-7g-5p
## hsa-miR-1287-5p hsa-miR-1287-5p
## hsa-miR-324-5p hsa-miR-324-5p
## hsa-miR-484 hsa-miR-484
## hsa-miR-183-5p hsa-miR-183-5p
## hsa-miR-582-5p hsa-miR-582-5p
## hsa-let-7b-5p hsa-let-7b-5p
## hsa-miR-16-5p hsa-miR-16-5p
## hsa-miR-331-3p hsa-miR-331-3p
## hsa-miR-148a-3p hsa-miR-148a-3p
## hsa-miR-98-5p hsa-miR-98-5p
## hsa-miR-26b-5p hsa-miR-26b-5p
## Phylum
## hsa-miR-222-3p hsa-miR-222-3p
## hsa-miR-151a-5p hsa-miR-151a-5p
## hsa-miR-424-5p hsa-miR-424-5p
## hsa-miR-503-5p hsa-miR-503-5p
## hsa-miR-129-2-3p hsa-miR-129-2-3p
## hsa-miR-1252-5p hsa-miR-1252-5p
## hsa-miR-1246 hsa-miR-1246
## hsa-miR-182-5p hsa-miR-182-5p
## hsa-miR-1205 hsa-miR-1205
## hsa-miR-450a-5p hsa-miR-450a-5p
## hsa-miR-654-5p hsa-miR-654-5p
## hsa-miR-519d-3p hsa-miR-519d-3p
## hsa-miR-4431 hsa-miR-4431
## hsa-miR-345-5p hsa-miR-345-5p
## hsa-miR-3934-5p hsa-miR-3934-5p
## hsa-miR-221-5p hsa-miR-221-5p
## hsa-miR-152-3p hsa-miR-152-3p
## hsa-miR-1260b hsa-miR-1260b
## hsa-miR-34a-5p hsa-miR-34a-5p
## hsa-miR-150-5p hsa-miR-150-5p
## hsa-let-7d-5p hsa-let-7d-5p
## hsa-miR-483-3p hsa-miR-483-3p
## hsa-miR-146b-5p hsa-miR-146b-5p
## hsa-miR-371b-5p hsa-miR-371b-5p
## hsa-miR-601 hsa-miR-601
## hsa-miR-5010-3p hsa-miR-5010-3p
## hsa-miR-591 hsa-miR-591
## hsa-miR-155-5p hsa-miR-155-5p
## hsa-miR-363-3p hsa-miR-363-3p
## hsa-miR-3916 hsa-miR-3916
## hsa-let-7i-5p hsa-let-7i-5p
## hsa-miR-1827 hsa-miR-1827
## hsa-miR-4455 hsa-miR-4455
## hsa-miR-3195 hsa-miR-3195
## hsa-miR-195-5p hsa-miR-195-5p
## hsa-miR-1226-3p hsa-miR-1226-3p
## hsa-miR-27a-3p hsa-miR-27a-3p
## hsa-miR-194-5p hsa-miR-194-5p
## hsa-miR-28-3p hsa-miR-28-3p
## hsa-miR-151a-3p hsa-miR-151a-3p
## hsa-miR-10a-5p hsa-miR-10a-5p
## hsa-miR-146a-5p hsa-miR-146a-5p
## hsa-miR-337-3p hsa-miR-337-3p
## hsa-miR-200c-3p hsa-miR-200c-3p
## hsa-miR-96-5p hsa-miR-96-5p
## hsa-miR-5196-3p+hsa-miR-6732-3p hsa-miR-5196-3p+hsa-miR-6732-3p
## hsa-miR-551b-3p hsa-miR-551b-3p
## hsa-miR-299-3p hsa-miR-299-3p
## hsa-let-7f-5p hsa-let-7f-5p
## hsa-miR-577 hsa-miR-577
## hsa-miR-181b-2-3p hsa-miR-181b-2-3p
## hsa-let-7g-5p hsa-let-7g-5p
## hsa-miR-1287-5p hsa-miR-1287-5p
## hsa-miR-324-5p hsa-miR-324-5p
## hsa-miR-484 hsa-miR-484
## hsa-miR-183-5p hsa-miR-183-5p
## hsa-miR-582-5p hsa-miR-582-5p
## hsa-let-7b-5p hsa-let-7b-5p
## hsa-miR-16-5p hsa-miR-16-5p
## hsa-miR-331-3p hsa-miR-331-3p
## hsa-miR-148a-3p hsa-miR-148a-3p
## hsa-miR-98-5p hsa-miR-98-5p
## hsa-miR-26b-5p hsa-miR-26b-5p
## Class
## hsa-miR-222-3p hsa-miR-222-3p
## hsa-miR-151a-5p hsa-miR-151a-5p
## hsa-miR-424-5p hsa-miR-424-5p
## hsa-miR-503-5p hsa-miR-503-5p
## hsa-miR-129-2-3p hsa-miR-129-2-3p
## hsa-miR-1252-5p hsa-miR-1252-5p
## hsa-miR-1246 hsa-miR-1246
## hsa-miR-182-5p hsa-miR-182-5p
## hsa-miR-1205 hsa-miR-1205
## hsa-miR-450a-5p hsa-miR-450a-5p
## hsa-miR-654-5p hsa-miR-654-5p
## hsa-miR-519d-3p hsa-miR-519d-3p
## hsa-miR-4431 hsa-miR-4431
## hsa-miR-345-5p hsa-miR-345-5p
## hsa-miR-3934-5p hsa-miR-3934-5p
## hsa-miR-221-5p hsa-miR-221-5p
## hsa-miR-152-3p hsa-miR-152-3p
## hsa-miR-1260b hsa-miR-1260b
## hsa-miR-34a-5p hsa-miR-34a-5p
## hsa-miR-150-5p hsa-miR-150-5p
## hsa-let-7d-5p hsa-let-7d-5p
## hsa-miR-483-3p hsa-miR-483-3p
## hsa-miR-146b-5p hsa-miR-146b-5p
## hsa-miR-371b-5p hsa-miR-371b-5p
## hsa-miR-601 hsa-miR-601
## hsa-miR-5010-3p hsa-miR-5010-3p
## hsa-miR-591 hsa-miR-591
## hsa-miR-155-5p hsa-miR-155-5p
## hsa-miR-363-3p hsa-miR-363-3p
## hsa-miR-3916 hsa-miR-3916
## hsa-let-7i-5p hsa-let-7i-5p
## hsa-miR-1827 hsa-miR-1827
## hsa-miR-4455 hsa-miR-4455
## hsa-miR-3195 hsa-miR-3195
## hsa-miR-195-5p hsa-miR-195-5p
## hsa-miR-1226-3p hsa-miR-1226-3p
## hsa-miR-27a-3p hsa-miR-27a-3p
## hsa-miR-194-5p hsa-miR-194-5p
## hsa-miR-28-3p hsa-miR-28-3p
## hsa-miR-151a-3p hsa-miR-151a-3p
## hsa-miR-10a-5p hsa-miR-10a-5p
## hsa-miR-146a-5p hsa-miR-146a-5p
## hsa-miR-337-3p hsa-miR-337-3p
## hsa-miR-200c-3p hsa-miR-200c-3p
## hsa-miR-96-5p hsa-miR-96-5p
## hsa-miR-5196-3p+hsa-miR-6732-3p hsa-miR-5196-3p+hsa-miR-6732-3p
## hsa-miR-551b-3p hsa-miR-551b-3p
## hsa-miR-299-3p hsa-miR-299-3p
## hsa-let-7f-5p hsa-let-7f-5p
## hsa-miR-577 hsa-miR-577
## hsa-miR-181b-2-3p hsa-miR-181b-2-3p
## hsa-let-7g-5p hsa-let-7g-5p
## hsa-miR-1287-5p hsa-miR-1287-5p
## hsa-miR-324-5p hsa-miR-324-5p
## hsa-miR-484 hsa-miR-484
## hsa-miR-183-5p hsa-miR-183-5p
## hsa-miR-582-5p hsa-miR-582-5p
## hsa-let-7b-5p hsa-let-7b-5p
## hsa-miR-16-5p hsa-miR-16-5p
## hsa-miR-331-3p hsa-miR-331-3p
## hsa-miR-148a-3p hsa-miR-148a-3p
## hsa-miR-98-5p hsa-miR-98-5p
## hsa-miR-26b-5p hsa-miR-26b-5p
## Order
## hsa-miR-222-3p hsa-miR-222-3p
## hsa-miR-151a-5p hsa-miR-151a-5p
## hsa-miR-424-5p hsa-miR-424-5p
## hsa-miR-503-5p hsa-miR-503-5p
## hsa-miR-129-2-3p hsa-miR-129-2-3p
## hsa-miR-1252-5p hsa-miR-1252-5p
## hsa-miR-1246 hsa-miR-1246
## hsa-miR-182-5p hsa-miR-182-5p
## hsa-miR-1205 hsa-miR-1205
## hsa-miR-450a-5p hsa-miR-450a-5p
## hsa-miR-654-5p hsa-miR-654-5p
## hsa-miR-519d-3p hsa-miR-519d-3p
## hsa-miR-4431 hsa-miR-4431
## hsa-miR-345-5p hsa-miR-345-5p
## hsa-miR-3934-5p hsa-miR-3934-5p
## hsa-miR-221-5p hsa-miR-221-5p
## hsa-miR-152-3p hsa-miR-152-3p
## hsa-miR-1260b hsa-miR-1260b
## hsa-miR-34a-5p hsa-miR-34a-5p
## hsa-miR-150-5p hsa-miR-150-5p
## hsa-let-7d-5p hsa-let-7d-5p
## hsa-miR-483-3p hsa-miR-483-3p
## hsa-miR-146b-5p hsa-miR-146b-5p
## hsa-miR-371b-5p hsa-miR-371b-5p
## hsa-miR-601 hsa-miR-601
## hsa-miR-5010-3p hsa-miR-5010-3p
## hsa-miR-591 hsa-miR-591
## hsa-miR-155-5p hsa-miR-155-5p
## hsa-miR-363-3p hsa-miR-363-3p
## hsa-miR-3916 hsa-miR-3916
## hsa-let-7i-5p hsa-let-7i-5p
## hsa-miR-1827 hsa-miR-1827
## hsa-miR-4455 hsa-miR-4455
## hsa-miR-3195 hsa-miR-3195
## hsa-miR-195-5p hsa-miR-195-5p
## hsa-miR-1226-3p hsa-miR-1226-3p
## hsa-miR-27a-3p hsa-miR-27a-3p
## hsa-miR-194-5p hsa-miR-194-5p
## hsa-miR-28-3p hsa-miR-28-3p
## hsa-miR-151a-3p hsa-miR-151a-3p
## hsa-miR-10a-5p hsa-miR-10a-5p
## hsa-miR-146a-5p hsa-miR-146a-5p
## hsa-miR-337-3p hsa-miR-337-3p
## hsa-miR-200c-3p hsa-miR-200c-3p
## hsa-miR-96-5p hsa-miR-96-5p
## hsa-miR-5196-3p+hsa-miR-6732-3p hsa-miR-5196-3p+hsa-miR-6732-3p
## hsa-miR-551b-3p hsa-miR-551b-3p
## hsa-miR-299-3p hsa-miR-299-3p
## hsa-let-7f-5p hsa-let-7f-5p
## hsa-miR-577 hsa-miR-577
## hsa-miR-181b-2-3p hsa-miR-181b-2-3p
## hsa-let-7g-5p hsa-let-7g-5p
## hsa-miR-1287-5p hsa-miR-1287-5p
## hsa-miR-324-5p hsa-miR-324-5p
## hsa-miR-484 hsa-miR-484
## hsa-miR-183-5p hsa-miR-183-5p
## hsa-miR-582-5p hsa-miR-582-5p
## hsa-let-7b-5p hsa-let-7b-5p
## hsa-miR-16-5p hsa-miR-16-5p
## hsa-miR-331-3p hsa-miR-331-3p
## hsa-miR-148a-3p hsa-miR-148a-3p
## hsa-miR-98-5p hsa-miR-98-5p
## hsa-miR-26b-5p hsa-miR-26b-5p
## Family
## hsa-miR-222-3p hsa-miR-222-3p
## hsa-miR-151a-5p hsa-miR-151a-5p
## hsa-miR-424-5p hsa-miR-424-5p
## hsa-miR-503-5p hsa-miR-503-5p
## hsa-miR-129-2-3p hsa-miR-129-2-3p
## hsa-miR-1252-5p hsa-miR-1252-5p
## hsa-miR-1246 hsa-miR-1246
## hsa-miR-182-5p hsa-miR-182-5p
## hsa-miR-1205 hsa-miR-1205
## hsa-miR-450a-5p hsa-miR-450a-5p
## hsa-miR-654-5p hsa-miR-654-5p
## hsa-miR-519d-3p hsa-miR-519d-3p
## hsa-miR-4431 hsa-miR-4431
## hsa-miR-345-5p hsa-miR-345-5p
## hsa-miR-3934-5p hsa-miR-3934-5p
## hsa-miR-221-5p hsa-miR-221-5p
## hsa-miR-152-3p hsa-miR-152-3p
## hsa-miR-1260b hsa-miR-1260b
## hsa-miR-34a-5p hsa-miR-34a-5p
## hsa-miR-150-5p hsa-miR-150-5p
## hsa-let-7d-5p hsa-let-7d-5p
## hsa-miR-483-3p hsa-miR-483-3p
## hsa-miR-146b-5p hsa-miR-146b-5p
## hsa-miR-371b-5p hsa-miR-371b-5p
## hsa-miR-601 hsa-miR-601
## hsa-miR-5010-3p hsa-miR-5010-3p
## hsa-miR-591 hsa-miR-591
## hsa-miR-155-5p hsa-miR-155-5p
## hsa-miR-363-3p hsa-miR-363-3p
## hsa-miR-3916 hsa-miR-3916
## hsa-let-7i-5p hsa-let-7i-5p
## hsa-miR-1827 hsa-miR-1827
## hsa-miR-4455 hsa-miR-4455
## hsa-miR-3195 hsa-miR-3195
## hsa-miR-195-5p hsa-miR-195-5p
## hsa-miR-1226-3p hsa-miR-1226-3p
## hsa-miR-27a-3p hsa-miR-27a-3p
## hsa-miR-194-5p hsa-miR-194-5p
## hsa-miR-28-3p hsa-miR-28-3p
## hsa-miR-151a-3p hsa-miR-151a-3p
## hsa-miR-10a-5p hsa-miR-10a-5p
## hsa-miR-146a-5p hsa-miR-146a-5p
## hsa-miR-337-3p hsa-miR-337-3p
## hsa-miR-200c-3p hsa-miR-200c-3p
## hsa-miR-96-5p hsa-miR-96-5p
## hsa-miR-5196-3p+hsa-miR-6732-3p hsa-miR-5196-3p+hsa-miR-6732-3p
## hsa-miR-551b-3p hsa-miR-551b-3p
## hsa-miR-299-3p hsa-miR-299-3p
## hsa-let-7f-5p hsa-let-7f-5p
## hsa-miR-577 hsa-miR-577
## hsa-miR-181b-2-3p hsa-miR-181b-2-3p
## hsa-let-7g-5p hsa-let-7g-5p
## hsa-miR-1287-5p hsa-miR-1287-5p
## hsa-miR-324-5p hsa-miR-324-5p
## hsa-miR-484 hsa-miR-484
## hsa-miR-183-5p hsa-miR-183-5p
## hsa-miR-582-5p hsa-miR-582-5p
## hsa-let-7b-5p hsa-let-7b-5p
## hsa-miR-16-5p hsa-miR-16-5p
## hsa-miR-331-3p hsa-miR-331-3p
## hsa-miR-148a-3p hsa-miR-148a-3p
## hsa-miR-98-5p hsa-miR-98-5p
## hsa-miR-26b-5p hsa-miR-26b-5p
## Genus
## hsa-miR-222-3p hsa-miR-222-3p
## hsa-miR-151a-5p hsa-miR-151a-5p
## hsa-miR-424-5p hsa-miR-424-5p
## hsa-miR-503-5p hsa-miR-503-5p
## hsa-miR-129-2-3p hsa-miR-129-2-3p
## hsa-miR-1252-5p hsa-miR-1252-5p
## hsa-miR-1246 hsa-miR-1246
## hsa-miR-182-5p hsa-miR-182-5p
## hsa-miR-1205 hsa-miR-1205
## hsa-miR-450a-5p hsa-miR-450a-5p
## hsa-miR-654-5p hsa-miR-654-5p
## hsa-miR-519d-3p hsa-miR-519d-3p
## hsa-miR-4431 hsa-miR-4431
## hsa-miR-345-5p hsa-miR-345-5p
## hsa-miR-3934-5p hsa-miR-3934-5p
## hsa-miR-221-5p hsa-miR-221-5p
## hsa-miR-152-3p hsa-miR-152-3p
## hsa-miR-1260b hsa-miR-1260b
## hsa-miR-34a-5p hsa-miR-34a-5p
## hsa-miR-150-5p hsa-miR-150-5p
## hsa-let-7d-5p hsa-let-7d-5p
## hsa-miR-483-3p hsa-miR-483-3p
## hsa-miR-146b-5p hsa-miR-146b-5p
## hsa-miR-371b-5p hsa-miR-371b-5p
## hsa-miR-601 hsa-miR-601
## hsa-miR-5010-3p hsa-miR-5010-3p
## hsa-miR-591 hsa-miR-591
## hsa-miR-155-5p hsa-miR-155-5p
## hsa-miR-363-3p hsa-miR-363-3p
## hsa-miR-3916 hsa-miR-3916
## hsa-let-7i-5p hsa-let-7i-5p
## hsa-miR-1827 hsa-miR-1827
## hsa-miR-4455 hsa-miR-4455
## hsa-miR-3195 hsa-miR-3195
## hsa-miR-195-5p hsa-miR-195-5p
## hsa-miR-1226-3p hsa-miR-1226-3p
## hsa-miR-27a-3p hsa-miR-27a-3p
## hsa-miR-194-5p hsa-miR-194-5p
## hsa-miR-28-3p hsa-miR-28-3p
## hsa-miR-151a-3p hsa-miR-151a-3p
## hsa-miR-10a-5p hsa-miR-10a-5p
## hsa-miR-146a-5p hsa-miR-146a-5p
## hsa-miR-337-3p hsa-miR-337-3p
## hsa-miR-200c-3p hsa-miR-200c-3p
## hsa-miR-96-5p hsa-miR-96-5p
## hsa-miR-5196-3p+hsa-miR-6732-3p hsa-miR-5196-3p+hsa-miR-6732-3p
## hsa-miR-551b-3p hsa-miR-551b-3p
## hsa-miR-299-3p hsa-miR-299-3p
## hsa-let-7f-5p hsa-let-7f-5p
## hsa-miR-577 hsa-miR-577
## hsa-miR-181b-2-3p hsa-miR-181b-2-3p
## hsa-let-7g-5p hsa-let-7g-5p
## hsa-miR-1287-5p hsa-miR-1287-5p
## hsa-miR-324-5p hsa-miR-324-5p
## hsa-miR-484 hsa-miR-484
## hsa-miR-183-5p hsa-miR-183-5p
## hsa-miR-582-5p hsa-miR-582-5p
## hsa-let-7b-5p hsa-let-7b-5p
## hsa-miR-16-5p hsa-miR-16-5p
## hsa-miR-331-3p hsa-miR-331-3p
## hsa-miR-148a-3p hsa-miR-148a-3p
## hsa-miR-98-5p hsa-miR-98-5p
## hsa-miR-26b-5p hsa-miR-26b-5p
## Species
## hsa-miR-222-3p hsa-miR-222-3p
## hsa-miR-151a-5p hsa-miR-151a-5p
## hsa-miR-424-5p hsa-miR-424-5p
## hsa-miR-503-5p hsa-miR-503-5p
## hsa-miR-129-2-3p hsa-miR-129-2-3p
## hsa-miR-1252-5p hsa-miR-1252-5p
## hsa-miR-1246 hsa-miR-1246
## hsa-miR-182-5p hsa-miR-182-5p
## hsa-miR-1205 hsa-miR-1205
## hsa-miR-450a-5p hsa-miR-450a-5p
## hsa-miR-654-5p hsa-miR-654-5p
## hsa-miR-519d-3p hsa-miR-519d-3p
## hsa-miR-4431 hsa-miR-4431
## hsa-miR-345-5p hsa-miR-345-5p
## hsa-miR-3934-5p hsa-miR-3934-5p
## hsa-miR-221-5p hsa-miR-221-5p
## hsa-miR-152-3p hsa-miR-152-3p
## hsa-miR-1260b hsa-miR-1260b
## hsa-miR-34a-5p hsa-miR-34a-5p
## hsa-miR-150-5p hsa-miR-150-5p
## hsa-let-7d-5p hsa-let-7d-5p
## hsa-miR-483-3p hsa-miR-483-3p
## hsa-miR-146b-5p hsa-miR-146b-5p
## hsa-miR-371b-5p hsa-miR-371b-5p
## hsa-miR-601 hsa-miR-601
## hsa-miR-5010-3p hsa-miR-5010-3p
## hsa-miR-591 hsa-miR-591
## hsa-miR-155-5p hsa-miR-155-5p
## hsa-miR-363-3p hsa-miR-363-3p
## hsa-miR-3916 hsa-miR-3916
## hsa-let-7i-5p hsa-let-7i-5p
## hsa-miR-1827 hsa-miR-1827
## hsa-miR-4455 hsa-miR-4455
## hsa-miR-3195 hsa-miR-3195
## hsa-miR-195-5p hsa-miR-195-5p
## hsa-miR-1226-3p hsa-miR-1226-3p
## hsa-miR-27a-3p hsa-miR-27a-3p
## hsa-miR-194-5p hsa-miR-194-5p
## hsa-miR-28-3p hsa-miR-28-3p
## hsa-miR-151a-3p hsa-miR-151a-3p
## hsa-miR-10a-5p hsa-miR-10a-5p
## hsa-miR-146a-5p hsa-miR-146a-5p
## hsa-miR-337-3p hsa-miR-337-3p
## hsa-miR-200c-3p hsa-miR-200c-3p
## hsa-miR-96-5p hsa-miR-96-5p
## hsa-miR-5196-3p+hsa-miR-6732-3p hsa-miR-5196-3p+hsa-miR-6732-3p
## hsa-miR-551b-3p hsa-miR-551b-3p
## hsa-miR-299-3p hsa-miR-299-3p
## hsa-let-7f-5p hsa-let-7f-5p
## hsa-miR-577 hsa-miR-577
## hsa-miR-181b-2-3p hsa-miR-181b-2-3p
## hsa-let-7g-5p hsa-let-7g-5p
## hsa-miR-1287-5p hsa-miR-1287-5p
## hsa-miR-324-5p hsa-miR-324-5p
## hsa-miR-484 hsa-miR-484
## hsa-miR-183-5p hsa-miR-183-5p
## hsa-miR-582-5p hsa-miR-582-5p
## hsa-let-7b-5p hsa-let-7b-5p
## hsa-miR-16-5p hsa-miR-16-5p
## hsa-miR-331-3p hsa-miR-331-3p
## hsa-miR-148a-3p hsa-miR-148a-3p
## hsa-miR-98-5p hsa-miR-98-5p
## hsa-miR-26b-5p hsa-miR-26b-5p#########################################################################################################################
ush2.nbt.res = results(dds, contrast =c("phenotype","USHtype2","NBT_A") )
ush2.nbt.res = ush2.nbt.res[order(ush2.nbt.res$padj, na.last=NA), ]
ush2.nbt.sigtab = ush2.nbt.res[(ush2.nbt.res$padj < alpha), ]
ush2.nbt.sigtab = cbind(as(ush2.nbt.sigtab, "data.frame"), as(tax_table(uvc.ps)[rownames(ush2.nbt.sigtab), ], "matrix"))
#order df by log fold change and manually inspect where 2fold change threshold is, in this case row 76:
o = order(abs(ush2.nbt.sigtab$log2FoldChange), decreasing = TRUE)
ush2.nbt.sigtab2 = ush2.nbt.sigtab[o, ]
ush2.nbt.sigtab2 = ush2.nbt.sigtab2[1:62,]
ush2.nbt.sigtab2## baseMean log2FoldChange lfcSE stat
## hsa-miR-150-5p 452.972299 9.778649 0.6146488 15.909327
## hsa-miR-222-3p 62.352476 8.870729 1.0944227 8.105396
## hsa-miR-424-5p 26.056195 8.157197 1.5433530 5.285373
## hsa-miR-182-5p 13.386544 -8.037607 1.9945504 -4.029784
## hsa-miR-503-5p 22.185375 7.681362 1.1005214 6.979748
## hsa-miR-450a-5p 22.279584 7.297461 1.6271085 4.484926
## hsa-miR-1252-5p 14.162051 7.074685 1.1272707 6.275942
## hsa-miR-1205 11.550695 6.445804 1.1330601 5.688845
## hsa-miR-146b-5p 168.632116 6.088318 0.4802753 12.676724
## hsa-let-7d-5p 588.882373 5.638169 0.3829947 14.721274
## hsa-miR-574-3p 3.881123 5.375367 1.5182670 3.540462
## hsa-miR-34a-5p 454.895501 5.311438 0.4715676 11.263365
## hsa-miR-513b-5p 4.472696 -5.288974 1.5553773 -3.400444
## hsa-miR-483-3p 7.946179 5.196772 1.1651060 4.460343
## hsa-miR-3934-5p 5.185416 5.101567 1.3401854 3.806613
## hsa-miR-654-5p 5.891644 5.045064 1.2956111 3.893965
## hsa-miR-137 3.212125 -4.918130 1.5168913 -3.242243
## hsa-miR-223-3p 159.998728 4.803963 0.4137021 11.612131
## hsa-miR-221-5p 4.368518 4.752997 1.7215001 2.760962
## hsa-miR-28-3p 59.451504 -4.730881 0.5026720 -9.411468
## hsa-miR-155-5p 11097.869650 4.641151 0.3023737 15.349056
## hsa-miR-5010-3p 3.921794 4.593808 1.4696719 3.125737
## hsa-let-7i-5p 1348.149768 4.591980 0.3728104 12.317198
## hsa-miR-591 6.943709 4.531152 1.2811069 3.536904
## hsa-miR-370-3p 7.208119 -4.505703 1.2364461 -3.644075
## hsa-miR-503-3p 3.150306 4.501648 1.6023650 2.809377
## hsa-miR-577 4.625677 -4.182267 1.2823862 -3.261316
## hsa-miR-146a-5p 4963.645408 3.645911 0.4239996 8.598854
## hsa-miR-98-5p 271.663474 3.591315 0.3694847 9.719795
## hsa-miR-767-5p 11.339515 -3.547808 0.8331848 -4.258128
## hsa-let-7g-5p 2774.856795 3.535712 0.3288041 10.753248
## hsa-miR-200c-3p 16.731190 3.532417 0.7472186 4.727421
## hsa-miR-1246 1161.454061 -3.461864 0.4797713 -7.215653
## hsa-miR-363-3p 319.076852 3.349837 0.3439406 9.739580
## hsa-miR-96-5p 34.142701 -3.270057 0.3932114 -8.316283
## hsa-miR-132-3p 128.534206 3.121741 0.4182450 7.463904
## hsa-miR-194-5p 42.517210 -3.012283 0.4281448 -7.035664
## hsa-let-7b-5p 108.579356 2.885862 0.2926133 9.862373
## hsa-let-7f-5p 763.539513 2.770965 0.3640103 7.612326
## hsa-miR-183-5p 36.030068 -2.679509 0.4253317 -6.299810
## hsa-miR-1183 14.842391 -2.677247 0.6562571 -4.079570
## hsa-miR-148a-3p 543.177533 2.670289 0.3797355 7.031972
## hsa-miR-28-5p 61.342319 -2.573117 0.4597829 -5.596373
## hsa-miR-542-5p 10.002203 2.563074 0.7582140 3.380409
## hsa-let-7e-5p 9.166687 2.553093 0.7141781 3.574868
## hsa-miR-23c 12.349246 -2.420985 0.7402905 -3.270317
## hsa-miR-337-3p 54.247834 2.391787 0.4919785 4.861568
## hsa-miR-5196-3p+hsa-miR-6732-3p 14.758482 2.372361 0.6845475 3.465590
## hsa-miR-551b-3p 33.524252 2.320197 0.4506935 5.148060
## hsa-miR-10a-5p 44.111464 2.318068 0.4528395 5.118962
## hsa-miR-299-3p 27.522827 -2.209553 0.7432988 -2.972631
## hsa-miR-221-3p 18.166067 2.201322 0.8422616 2.613584
## hsa-miR-374b-5p 119.433283 2.147022 0.4597624 4.669851
## hsa-miR-23a-3p 414.360207 2.131325 0.2983583 7.143508
## hsa-let-7c-5p 41.178953 2.119217 0.3306348 6.409541
## hsa-miR-1304-5p 14.303492 2.107587 0.5285978 3.987127
## hsa-miR-514a-3p 14.397073 -2.079384 0.6107492 -3.404645
## hsa-let-7a-5p 3774.101892 2.076836 0.3213073 6.463705
## hsa-miR-342-3p 345.669854 2.057311 0.3992218 5.153304
## hsa-miR-181a-5p 961.247540 -2.034583 0.4444699 -4.577551
## hsa-miR-181c-5p 21.150719 -2.027716 0.5632067 -3.600306
## hsa-miR-1193 21.196875 2.010495 0.4499887 4.467878
## pvalue padj
## hsa-miR-150-5p 5.459739e-57 2.647974e-54
## hsa-miR-222-3p 5.257388e-16 1.699889e-14
## hsa-miR-424-5p 1.254486e-07 1.789487e-06
## hsa-miR-182-5p 5.582820e-05 5.014200e-04
## hsa-miR-503-5p 2.957100e-12 6.235624e-11
## hsa-miR-450a-5p 7.293916e-06 7.861220e-05
## hsa-miR-1252-5p 3.475246e-10 6.242572e-09
## hsa-miR-1205 1.279012e-08 2.215431e-07
## hsa-miR-146b-5p 7.958962e-37 9.650242e-35
## hsa-let-7d-5p 4.707086e-49 7.609790e-47
## hsa-miR-574-3p 3.994268e-04 2.887511e-03
## hsa-miR-34a-5p 1.990115e-29 1.378865e-27
## hsa-miR-513b-5p 6.727649e-04 4.531819e-03
## hsa-miR-483-3p 8.182875e-06 8.444030e-05
## hsa-miR-3934-5p 1.408829e-04 1.138804e-03
## hsa-miR-654-5p 9.861905e-05 8.246593e-04
## hsa-miR-137 1.185929e-03 7.374049e-03
## hsa-miR-223-3p 3.575911e-31 2.890528e-29
## hsa-miR-221-5p 5.763134e-03 3.071560e-02
## hsa-miR-28-3p 4.892560e-21 1.977410e-19
## hsa-miR-155-5p 3.593791e-53 8.714944e-51
## hsa-miR-5010-3p 1.773601e-03 1.061971e-02
## hsa-let-7i-5p 7.319374e-35 7.099793e-33
## hsa-miR-591 4.048469e-04 2.887511e-03
## hsa-miR-370-3p 2.683551e-04 2.133643e-03
## hsa-miR-503-3p 4.963743e-03 2.735699e-02
## hsa-miR-577 1.108963e-03 6.985028e-03
## hsa-miR-146a-5p 8.051598e-18 3.003865e-16
## hsa-miR-98-5p 2.482818e-22 1.094697e-20
## hsa-miR-767-5p 2.061458e-05 1.999614e-04
## hsa-let-7g-5p 5.721061e-27 3.468393e-25
## hsa-miR-200c-3p 2.273890e-06 2.757091e-05
## hsa-miR-1246 5.367563e-13 1.370141e-11
## hsa-miR-363-3p 2.043964e-22 9.913224e-21
## hsa-miR-96-5p 9.076483e-17 3.144353e-15
## hsa-miR-132-3p 8.399554e-14 2.263213e-12
## hsa-miR-194-5p 1.983141e-12 4.489229e-11
## hsa-let-7b-5p 6.060009e-23 3.265672e-21
## hsa-let-7f-5p 2.692060e-14 7.680289e-13
## hsa-miR-183-5p 2.980105e-10 5.559043e-09
## hsa-miR-1183 4.511901e-05 4.128815e-04
## hsa-miR-148a-3p 2.036351e-12 4.489229e-11
## hsa-miR-28-5p 2.188825e-08 3.660622e-07
## hsa-miR-542-5p 7.237793e-04 4.743688e-03
## hsa-let-7e-5p 3.504045e-04 2.614556e-03
## hsa-miR-23c 1.074270e-03 6.855541e-03
## hsa-miR-337-3p 1.164597e-06 1.448281e-05
## hsa-miR-5196-3p+hsa-miR-6732-3p 5.290697e-04 3.665697e-03
## hsa-miR-551b-3p 2.631947e-07 3.545817e-06
## hsa-miR-10a-5p 3.072225e-07 4.027106e-06
## hsa-miR-299-3p 2.952593e-03 1.746350e-02
## hsa-miR-221-3p 8.959795e-03 4.302476e-02
## hsa-miR-374b-5p 3.014182e-06 3.480663e-05
## hsa-miR-23a-3p 9.097836e-13 2.206225e-11
## hsa-let-7c-5p 1.459588e-10 2.831600e-09
## hsa-miR-1304-5p 6.687834e-05 5.897453e-04
## hsa-miR-514a-3p 6.625002e-04 4.525530e-03
## hsa-let-7a-5p 1.021703e-10 2.064691e-09
## hsa-miR-342-3p 2.559368e-07 3.545817e-06
## hsa-miR-181a-5p 4.704513e-06 5.306253e-05
## hsa-miR-181c-5p 3.178431e-04 2.427007e-03
## hsa-miR-1193 7.899920e-06 8.329264e-05
## Kingdom
## hsa-miR-150-5p hsa-miR-150-5p
## hsa-miR-222-3p hsa-miR-222-3p
## hsa-miR-424-5p hsa-miR-424-5p
## hsa-miR-182-5p hsa-miR-182-5p
## hsa-miR-503-5p hsa-miR-503-5p
## hsa-miR-450a-5p hsa-miR-450a-5p
## hsa-miR-1252-5p hsa-miR-1252-5p
## hsa-miR-1205 hsa-miR-1205
## hsa-miR-146b-5p hsa-miR-146b-5p
## hsa-let-7d-5p hsa-let-7d-5p
## hsa-miR-574-3p hsa-miR-574-3p
## hsa-miR-34a-5p hsa-miR-34a-5p
## hsa-miR-513b-5p hsa-miR-513b-5p
## hsa-miR-483-3p hsa-miR-483-3p
## hsa-miR-3934-5p hsa-miR-3934-5p
## hsa-miR-654-5p hsa-miR-654-5p
## hsa-miR-137 hsa-miR-137
## hsa-miR-223-3p hsa-miR-223-3p
## hsa-miR-221-5p hsa-miR-221-5p
## hsa-miR-28-3p hsa-miR-28-3p
## hsa-miR-155-5p hsa-miR-155-5p
## hsa-miR-5010-3p hsa-miR-5010-3p
## hsa-let-7i-5p hsa-let-7i-5p
## hsa-miR-591 hsa-miR-591
## hsa-miR-370-3p hsa-miR-370-3p
## hsa-miR-503-3p hsa-miR-503-3p
## hsa-miR-577 hsa-miR-577
## hsa-miR-146a-5p hsa-miR-146a-5p
## hsa-miR-98-5p hsa-miR-98-5p
## hsa-miR-767-5p hsa-miR-767-5p
## hsa-let-7g-5p hsa-let-7g-5p
## hsa-miR-200c-3p hsa-miR-200c-3p
## hsa-miR-1246 hsa-miR-1246
## hsa-miR-363-3p hsa-miR-363-3p
## hsa-miR-96-5p hsa-miR-96-5p
## hsa-miR-132-3p hsa-miR-132-3p
## hsa-miR-194-5p hsa-miR-194-5p
## hsa-let-7b-5p hsa-let-7b-5p
## hsa-let-7f-5p hsa-let-7f-5p
## hsa-miR-183-5p hsa-miR-183-5p
## hsa-miR-1183 hsa-miR-1183
## hsa-miR-148a-3p hsa-miR-148a-3p
## hsa-miR-28-5p hsa-miR-28-5p
## hsa-miR-542-5p hsa-miR-542-5p
## hsa-let-7e-5p hsa-let-7e-5p
## hsa-miR-23c hsa-miR-23c
## hsa-miR-337-3p hsa-miR-337-3p
## hsa-miR-5196-3p+hsa-miR-6732-3p hsa-miR-5196-3p+hsa-miR-6732-3p
## hsa-miR-551b-3p hsa-miR-551b-3p
## hsa-miR-10a-5p hsa-miR-10a-5p
## hsa-miR-299-3p hsa-miR-299-3p
## hsa-miR-221-3p hsa-miR-221-3p
## hsa-miR-374b-5p hsa-miR-374b-5p
## hsa-miR-23a-3p hsa-miR-23a-3p
## hsa-let-7c-5p hsa-let-7c-5p
## hsa-miR-1304-5p hsa-miR-1304-5p
## hsa-miR-514a-3p hsa-miR-514a-3p
## hsa-let-7a-5p hsa-let-7a-5p
## hsa-miR-342-3p hsa-miR-342-3p
## hsa-miR-181a-5p hsa-miR-181a-5p
## hsa-miR-181c-5p hsa-miR-181c-5p
## hsa-miR-1193 hsa-miR-1193
## Phylum
## hsa-miR-150-5p hsa-miR-150-5p
## hsa-miR-222-3p hsa-miR-222-3p
## hsa-miR-424-5p hsa-miR-424-5p
## hsa-miR-182-5p hsa-miR-182-5p
## hsa-miR-503-5p hsa-miR-503-5p
## hsa-miR-450a-5p hsa-miR-450a-5p
## hsa-miR-1252-5p hsa-miR-1252-5p
## hsa-miR-1205 hsa-miR-1205
## hsa-miR-146b-5p hsa-miR-146b-5p
## hsa-let-7d-5p hsa-let-7d-5p
## hsa-miR-574-3p hsa-miR-574-3p
## hsa-miR-34a-5p hsa-miR-34a-5p
## hsa-miR-513b-5p hsa-miR-513b-5p
## hsa-miR-483-3p hsa-miR-483-3p
## hsa-miR-3934-5p hsa-miR-3934-5p
## hsa-miR-654-5p hsa-miR-654-5p
## hsa-miR-137 hsa-miR-137
## hsa-miR-223-3p hsa-miR-223-3p
## hsa-miR-221-5p hsa-miR-221-5p
## hsa-miR-28-3p hsa-miR-28-3p
## hsa-miR-155-5p hsa-miR-155-5p
## hsa-miR-5010-3p hsa-miR-5010-3p
## hsa-let-7i-5p hsa-let-7i-5p
## hsa-miR-591 hsa-miR-591
## hsa-miR-370-3p hsa-miR-370-3p
## hsa-miR-503-3p hsa-miR-503-3p
## hsa-miR-577 hsa-miR-577
## hsa-miR-146a-5p hsa-miR-146a-5p
## hsa-miR-98-5p hsa-miR-98-5p
## hsa-miR-767-5p hsa-miR-767-5p
## hsa-let-7g-5p hsa-let-7g-5p
## hsa-miR-200c-3p hsa-miR-200c-3p
## hsa-miR-1246 hsa-miR-1246
## hsa-miR-363-3p hsa-miR-363-3p
## hsa-miR-96-5p hsa-miR-96-5p
## hsa-miR-132-3p hsa-miR-132-3p
## hsa-miR-194-5p hsa-miR-194-5p
## hsa-let-7b-5p hsa-let-7b-5p
## hsa-let-7f-5p hsa-let-7f-5p
## hsa-miR-183-5p hsa-miR-183-5p
## hsa-miR-1183 hsa-miR-1183
## hsa-miR-148a-3p hsa-miR-148a-3p
## hsa-miR-28-5p hsa-miR-28-5p
## hsa-miR-542-5p hsa-miR-542-5p
## hsa-let-7e-5p hsa-let-7e-5p
## hsa-miR-23c hsa-miR-23c
## hsa-miR-337-3p hsa-miR-337-3p
## hsa-miR-5196-3p+hsa-miR-6732-3p hsa-miR-5196-3p+hsa-miR-6732-3p
## hsa-miR-551b-3p hsa-miR-551b-3p
## hsa-miR-10a-5p hsa-miR-10a-5p
## hsa-miR-299-3p hsa-miR-299-3p
## hsa-miR-221-3p hsa-miR-221-3p
## hsa-miR-374b-5p hsa-miR-374b-5p
## hsa-miR-23a-3p hsa-miR-23a-3p
## hsa-let-7c-5p hsa-let-7c-5p
## hsa-miR-1304-5p hsa-miR-1304-5p
## hsa-miR-514a-3p hsa-miR-514a-3p
## hsa-let-7a-5p hsa-let-7a-5p
## hsa-miR-342-3p hsa-miR-342-3p
## hsa-miR-181a-5p hsa-miR-181a-5p
## hsa-miR-181c-5p hsa-miR-181c-5p
## hsa-miR-1193 hsa-miR-1193
## Class
## hsa-miR-150-5p hsa-miR-150-5p
## hsa-miR-222-3p hsa-miR-222-3p
## hsa-miR-424-5p hsa-miR-424-5p
## hsa-miR-182-5p hsa-miR-182-5p
## hsa-miR-503-5p hsa-miR-503-5p
## hsa-miR-450a-5p hsa-miR-450a-5p
## hsa-miR-1252-5p hsa-miR-1252-5p
## hsa-miR-1205 hsa-miR-1205
## hsa-miR-146b-5p hsa-miR-146b-5p
## hsa-let-7d-5p hsa-let-7d-5p
## hsa-miR-574-3p hsa-miR-574-3p
## hsa-miR-34a-5p hsa-miR-34a-5p
## hsa-miR-513b-5p hsa-miR-513b-5p
## hsa-miR-483-3p hsa-miR-483-3p
## hsa-miR-3934-5p hsa-miR-3934-5p
## hsa-miR-654-5p hsa-miR-654-5p
## hsa-miR-137 hsa-miR-137
## hsa-miR-223-3p hsa-miR-223-3p
## hsa-miR-221-5p hsa-miR-221-5p
## hsa-miR-28-3p hsa-miR-28-3p
## hsa-miR-155-5p hsa-miR-155-5p
## hsa-miR-5010-3p hsa-miR-5010-3p
## hsa-let-7i-5p hsa-let-7i-5p
## hsa-miR-591 hsa-miR-591
## hsa-miR-370-3p hsa-miR-370-3p
## hsa-miR-503-3p hsa-miR-503-3p
## hsa-miR-577 hsa-miR-577
## hsa-miR-146a-5p hsa-miR-146a-5p
## hsa-miR-98-5p hsa-miR-98-5p
## hsa-miR-767-5p hsa-miR-767-5p
## hsa-let-7g-5p hsa-let-7g-5p
## hsa-miR-200c-3p hsa-miR-200c-3p
## hsa-miR-1246 hsa-miR-1246
## hsa-miR-363-3p hsa-miR-363-3p
## hsa-miR-96-5p hsa-miR-96-5p
## hsa-miR-132-3p hsa-miR-132-3p
## hsa-miR-194-5p hsa-miR-194-5p
## hsa-let-7b-5p hsa-let-7b-5p
## hsa-let-7f-5p hsa-let-7f-5p
## hsa-miR-183-5p hsa-miR-183-5p
## hsa-miR-1183 hsa-miR-1183
## hsa-miR-148a-3p hsa-miR-148a-3p
## hsa-miR-28-5p hsa-miR-28-5p
## hsa-miR-542-5p hsa-miR-542-5p
## hsa-let-7e-5p hsa-let-7e-5p
## hsa-miR-23c hsa-miR-23c
## hsa-miR-337-3p hsa-miR-337-3p
## hsa-miR-5196-3p+hsa-miR-6732-3p hsa-miR-5196-3p+hsa-miR-6732-3p
## hsa-miR-551b-3p hsa-miR-551b-3p
## hsa-miR-10a-5p hsa-miR-10a-5p
## hsa-miR-299-3p hsa-miR-299-3p
## hsa-miR-221-3p hsa-miR-221-3p
## hsa-miR-374b-5p hsa-miR-374b-5p
## hsa-miR-23a-3p hsa-miR-23a-3p
## hsa-let-7c-5p hsa-let-7c-5p
## hsa-miR-1304-5p hsa-miR-1304-5p
## hsa-miR-514a-3p hsa-miR-514a-3p
## hsa-let-7a-5p hsa-let-7a-5p
## hsa-miR-342-3p hsa-miR-342-3p
## hsa-miR-181a-5p hsa-miR-181a-5p
## hsa-miR-181c-5p hsa-miR-181c-5p
## hsa-miR-1193 hsa-miR-1193
## Order
## hsa-miR-150-5p hsa-miR-150-5p
## hsa-miR-222-3p hsa-miR-222-3p
## hsa-miR-424-5p hsa-miR-424-5p
## hsa-miR-182-5p hsa-miR-182-5p
## hsa-miR-503-5p hsa-miR-503-5p
## hsa-miR-450a-5p hsa-miR-450a-5p
## hsa-miR-1252-5p hsa-miR-1252-5p
## hsa-miR-1205 hsa-miR-1205
## hsa-miR-146b-5p hsa-miR-146b-5p
## hsa-let-7d-5p hsa-let-7d-5p
## hsa-miR-574-3p hsa-miR-574-3p
## hsa-miR-34a-5p hsa-miR-34a-5p
## hsa-miR-513b-5p hsa-miR-513b-5p
## hsa-miR-483-3p hsa-miR-483-3p
## hsa-miR-3934-5p hsa-miR-3934-5p
## hsa-miR-654-5p hsa-miR-654-5p
## hsa-miR-137 hsa-miR-137
## hsa-miR-223-3p hsa-miR-223-3p
## hsa-miR-221-5p hsa-miR-221-5p
## hsa-miR-28-3p hsa-miR-28-3p
## hsa-miR-155-5p hsa-miR-155-5p
## hsa-miR-5010-3p hsa-miR-5010-3p
## hsa-let-7i-5p hsa-let-7i-5p
## hsa-miR-591 hsa-miR-591
## hsa-miR-370-3p hsa-miR-370-3p
## hsa-miR-503-3p hsa-miR-503-3p
## hsa-miR-577 hsa-miR-577
## hsa-miR-146a-5p hsa-miR-146a-5p
## hsa-miR-98-5p hsa-miR-98-5p
## hsa-miR-767-5p hsa-miR-767-5p
## hsa-let-7g-5p hsa-let-7g-5p
## hsa-miR-200c-3p hsa-miR-200c-3p
## hsa-miR-1246 hsa-miR-1246
## hsa-miR-363-3p hsa-miR-363-3p
## hsa-miR-96-5p hsa-miR-96-5p
## hsa-miR-132-3p hsa-miR-132-3p
## hsa-miR-194-5p hsa-miR-194-5p
## hsa-let-7b-5p hsa-let-7b-5p
## hsa-let-7f-5p hsa-let-7f-5p
## hsa-miR-183-5p hsa-miR-183-5p
## hsa-miR-1183 hsa-miR-1183
## hsa-miR-148a-3p hsa-miR-148a-3p
## hsa-miR-28-5p hsa-miR-28-5p
## hsa-miR-542-5p hsa-miR-542-5p
## hsa-let-7e-5p hsa-let-7e-5p
## hsa-miR-23c hsa-miR-23c
## hsa-miR-337-3p hsa-miR-337-3p
## hsa-miR-5196-3p+hsa-miR-6732-3p hsa-miR-5196-3p+hsa-miR-6732-3p
## hsa-miR-551b-3p hsa-miR-551b-3p
## hsa-miR-10a-5p hsa-miR-10a-5p
## hsa-miR-299-3p hsa-miR-299-3p
## hsa-miR-221-3p hsa-miR-221-3p
## hsa-miR-374b-5p hsa-miR-374b-5p
## hsa-miR-23a-3p hsa-miR-23a-3p
## hsa-let-7c-5p hsa-let-7c-5p
## hsa-miR-1304-5p hsa-miR-1304-5p
## hsa-miR-514a-3p hsa-miR-514a-3p
## hsa-let-7a-5p hsa-let-7a-5p
## hsa-miR-342-3p hsa-miR-342-3p
## hsa-miR-181a-5p hsa-miR-181a-5p
## hsa-miR-181c-5p hsa-miR-181c-5p
## hsa-miR-1193 hsa-miR-1193
## Family
## hsa-miR-150-5p hsa-miR-150-5p
## hsa-miR-222-3p hsa-miR-222-3p
## hsa-miR-424-5p hsa-miR-424-5p
## hsa-miR-182-5p hsa-miR-182-5p
## hsa-miR-503-5p hsa-miR-503-5p
## hsa-miR-450a-5p hsa-miR-450a-5p
## hsa-miR-1252-5p hsa-miR-1252-5p
## hsa-miR-1205 hsa-miR-1205
## hsa-miR-146b-5p hsa-miR-146b-5p
## hsa-let-7d-5p hsa-let-7d-5p
## hsa-miR-574-3p hsa-miR-574-3p
## hsa-miR-34a-5p hsa-miR-34a-5p
## hsa-miR-513b-5p hsa-miR-513b-5p
## hsa-miR-483-3p hsa-miR-483-3p
## hsa-miR-3934-5p hsa-miR-3934-5p
## hsa-miR-654-5p hsa-miR-654-5p
## hsa-miR-137 hsa-miR-137
## hsa-miR-223-3p hsa-miR-223-3p
## hsa-miR-221-5p hsa-miR-221-5p
## hsa-miR-28-3p hsa-miR-28-3p
## hsa-miR-155-5p hsa-miR-155-5p
## hsa-miR-5010-3p hsa-miR-5010-3p
## hsa-let-7i-5p hsa-let-7i-5p
## hsa-miR-591 hsa-miR-591
## hsa-miR-370-3p hsa-miR-370-3p
## hsa-miR-503-3p hsa-miR-503-3p
## hsa-miR-577 hsa-miR-577
## hsa-miR-146a-5p hsa-miR-146a-5p
## hsa-miR-98-5p hsa-miR-98-5p
## hsa-miR-767-5p hsa-miR-767-5p
## hsa-let-7g-5p hsa-let-7g-5p
## hsa-miR-200c-3p hsa-miR-200c-3p
## hsa-miR-1246 hsa-miR-1246
## hsa-miR-363-3p hsa-miR-363-3p
## hsa-miR-96-5p hsa-miR-96-5p
## hsa-miR-132-3p hsa-miR-132-3p
## hsa-miR-194-5p hsa-miR-194-5p
## hsa-let-7b-5p hsa-let-7b-5p
## hsa-let-7f-5p hsa-let-7f-5p
## hsa-miR-183-5p hsa-miR-183-5p
## hsa-miR-1183 hsa-miR-1183
## hsa-miR-148a-3p hsa-miR-148a-3p
## hsa-miR-28-5p hsa-miR-28-5p
## hsa-miR-542-5p hsa-miR-542-5p
## hsa-let-7e-5p hsa-let-7e-5p
## hsa-miR-23c hsa-miR-23c
## hsa-miR-337-3p hsa-miR-337-3p
## hsa-miR-5196-3p+hsa-miR-6732-3p hsa-miR-5196-3p+hsa-miR-6732-3p
## hsa-miR-551b-3p hsa-miR-551b-3p
## hsa-miR-10a-5p hsa-miR-10a-5p
## hsa-miR-299-3p hsa-miR-299-3p
## hsa-miR-221-3p hsa-miR-221-3p
## hsa-miR-374b-5p hsa-miR-374b-5p
## hsa-miR-23a-3p hsa-miR-23a-3p
## hsa-let-7c-5p hsa-let-7c-5p
## hsa-miR-1304-5p hsa-miR-1304-5p
## hsa-miR-514a-3p hsa-miR-514a-3p
## hsa-let-7a-5p hsa-let-7a-5p
## hsa-miR-342-3p hsa-miR-342-3p
## hsa-miR-181a-5p hsa-miR-181a-5p
## hsa-miR-181c-5p hsa-miR-181c-5p
## hsa-miR-1193 hsa-miR-1193
## Genus
## hsa-miR-150-5p hsa-miR-150-5p
## hsa-miR-222-3p hsa-miR-222-3p
## hsa-miR-424-5p hsa-miR-424-5p
## hsa-miR-182-5p hsa-miR-182-5p
## hsa-miR-503-5p hsa-miR-503-5p
## hsa-miR-450a-5p hsa-miR-450a-5p
## hsa-miR-1252-5p hsa-miR-1252-5p
## hsa-miR-1205 hsa-miR-1205
## hsa-miR-146b-5p hsa-miR-146b-5p
## hsa-let-7d-5p hsa-let-7d-5p
## hsa-miR-574-3p hsa-miR-574-3p
## hsa-miR-34a-5p hsa-miR-34a-5p
## hsa-miR-513b-5p hsa-miR-513b-5p
## hsa-miR-483-3p hsa-miR-483-3p
## hsa-miR-3934-5p hsa-miR-3934-5p
## hsa-miR-654-5p hsa-miR-654-5p
## hsa-miR-137 hsa-miR-137
## hsa-miR-223-3p hsa-miR-223-3p
## hsa-miR-221-5p hsa-miR-221-5p
## hsa-miR-28-3p hsa-miR-28-3p
## hsa-miR-155-5p hsa-miR-155-5p
## hsa-miR-5010-3p hsa-miR-5010-3p
## hsa-let-7i-5p hsa-let-7i-5p
## hsa-miR-591 hsa-miR-591
## hsa-miR-370-3p hsa-miR-370-3p
## hsa-miR-503-3p hsa-miR-503-3p
## hsa-miR-577 hsa-miR-577
## hsa-miR-146a-5p hsa-miR-146a-5p
## hsa-miR-98-5p hsa-miR-98-5p
## hsa-miR-767-5p hsa-miR-767-5p
## hsa-let-7g-5p hsa-let-7g-5p
## hsa-miR-200c-3p hsa-miR-200c-3p
## hsa-miR-1246 hsa-miR-1246
## hsa-miR-363-3p hsa-miR-363-3p
## hsa-miR-96-5p hsa-miR-96-5p
## hsa-miR-132-3p hsa-miR-132-3p
## hsa-miR-194-5p hsa-miR-194-5p
## hsa-let-7b-5p hsa-let-7b-5p
## hsa-let-7f-5p hsa-let-7f-5p
## hsa-miR-183-5p hsa-miR-183-5p
## hsa-miR-1183 hsa-miR-1183
## hsa-miR-148a-3p hsa-miR-148a-3p
## hsa-miR-28-5p hsa-miR-28-5p
## hsa-miR-542-5p hsa-miR-542-5p
## hsa-let-7e-5p hsa-let-7e-5p
## hsa-miR-23c hsa-miR-23c
## hsa-miR-337-3p hsa-miR-337-3p
## hsa-miR-5196-3p+hsa-miR-6732-3p hsa-miR-5196-3p+hsa-miR-6732-3p
## hsa-miR-551b-3p hsa-miR-551b-3p
## hsa-miR-10a-5p hsa-miR-10a-5p
## hsa-miR-299-3p hsa-miR-299-3p
## hsa-miR-221-3p hsa-miR-221-3p
## hsa-miR-374b-5p hsa-miR-374b-5p
## hsa-miR-23a-3p hsa-miR-23a-3p
## hsa-let-7c-5p hsa-let-7c-5p
## hsa-miR-1304-5p hsa-miR-1304-5p
## hsa-miR-514a-3p hsa-miR-514a-3p
## hsa-let-7a-5p hsa-let-7a-5p
## hsa-miR-342-3p hsa-miR-342-3p
## hsa-miR-181a-5p hsa-miR-181a-5p
## hsa-miR-181c-5p hsa-miR-181c-5p
## hsa-miR-1193 hsa-miR-1193
## Species
## hsa-miR-150-5p hsa-miR-150-5p
## hsa-miR-222-3p hsa-miR-222-3p
## hsa-miR-424-5p hsa-miR-424-5p
## hsa-miR-182-5p hsa-miR-182-5p
## hsa-miR-503-5p hsa-miR-503-5p
## hsa-miR-450a-5p hsa-miR-450a-5p
## hsa-miR-1252-5p hsa-miR-1252-5p
## hsa-miR-1205 hsa-miR-1205
## hsa-miR-146b-5p hsa-miR-146b-5p
## hsa-let-7d-5p hsa-let-7d-5p
## hsa-miR-574-3p hsa-miR-574-3p
## hsa-miR-34a-5p hsa-miR-34a-5p
## hsa-miR-513b-5p hsa-miR-513b-5p
## hsa-miR-483-3p hsa-miR-483-3p
## hsa-miR-3934-5p hsa-miR-3934-5p
## hsa-miR-654-5p hsa-miR-654-5p
## hsa-miR-137 hsa-miR-137
## hsa-miR-223-3p hsa-miR-223-3p
## hsa-miR-221-5p hsa-miR-221-5p
## hsa-miR-28-3p hsa-miR-28-3p
## hsa-miR-155-5p hsa-miR-155-5p
## hsa-miR-5010-3p hsa-miR-5010-3p
## hsa-let-7i-5p hsa-let-7i-5p
## hsa-miR-591 hsa-miR-591
## hsa-miR-370-3p hsa-miR-370-3p
## hsa-miR-503-3p hsa-miR-503-3p
## hsa-miR-577 hsa-miR-577
## hsa-miR-146a-5p hsa-miR-146a-5p
## hsa-miR-98-5p hsa-miR-98-5p
## hsa-miR-767-5p hsa-miR-767-5p
## hsa-let-7g-5p hsa-let-7g-5p
## hsa-miR-200c-3p hsa-miR-200c-3p
## hsa-miR-1246 hsa-miR-1246
## hsa-miR-363-3p hsa-miR-363-3p
## hsa-miR-96-5p hsa-miR-96-5p
## hsa-miR-132-3p hsa-miR-132-3p
## hsa-miR-194-5p hsa-miR-194-5p
## hsa-let-7b-5p hsa-let-7b-5p
## hsa-let-7f-5p hsa-let-7f-5p
## hsa-miR-183-5p hsa-miR-183-5p
## hsa-miR-1183 hsa-miR-1183
## hsa-miR-148a-3p hsa-miR-148a-3p
## hsa-miR-28-5p hsa-miR-28-5p
## hsa-miR-542-5p hsa-miR-542-5p
## hsa-let-7e-5p hsa-let-7e-5p
## hsa-miR-23c hsa-miR-23c
## hsa-miR-337-3p hsa-miR-337-3p
## hsa-miR-5196-3p+hsa-miR-6732-3p hsa-miR-5196-3p+hsa-miR-6732-3p
## hsa-miR-551b-3p hsa-miR-551b-3p
## hsa-miR-10a-5p hsa-miR-10a-5p
## hsa-miR-299-3p hsa-miR-299-3p
## hsa-miR-221-3p hsa-miR-221-3p
## hsa-miR-374b-5p hsa-miR-374b-5p
## hsa-miR-23a-3p hsa-miR-23a-3p
## hsa-let-7c-5p hsa-let-7c-5p
## hsa-miR-1304-5p hsa-miR-1304-5p
## hsa-miR-514a-3p hsa-miR-514a-3p
## hsa-let-7a-5p hsa-let-7a-5p
## hsa-miR-342-3p hsa-miR-342-3p
## hsa-miR-181a-5p hsa-miR-181a-5p
## hsa-miR-181c-5p hsa-miR-181c-5p
## hsa-miR-1193 hsa-miR-1193########################################################################################################################
ush3.nbt.res = results(dds, contrast =c("phenotype","USHtype3","NBT_A") )
ush3.nbt.res = ush3.nbt.res[order(ush3.nbt.res$padj, na.last=NA), ]
ush3.nbt.sigtab = ush3.nbt.res[(ush3.nbt.res$padj < alpha), ]
ush3.nbt.sigtab = cbind(as(ush3.nbt.sigtab, "data.frame"), as(tax_table(uvc.ps)[rownames(ush3.nbt.sigtab), ], "matrix"))
#order df by log fold change and manually inspect where 2fold change threshold is, in this case row 76:
o = order(abs(ush3.nbt.sigtab$log2FoldChange), decreasing = TRUE)
ush3.nbt.sigtab2 = ush3.nbt.sigtab[o, ]
ush3.nbt.sigtab2 = ush3.nbt.sigtab2[1:56,]
ush3.nbt.sigtab2## baseMean log2FoldChange lfcSE
## hsa-miR-222-3p 62.352476 8.567235 1.0947828
## hsa-miR-503-5p 22.185375 7.436675 1.1010052
## hsa-miR-424-5p 26.056195 7.282262 1.5458130
## hsa-miR-150-5p 452.972299 6.589296 0.6167416
## hsa-miR-1205 11.550695 6.513342 1.1306670
## hsa-miR-450a-5p 22.279584 6.427184 1.6296560
## hsa-miR-1252-5p 14.162051 6.397175 1.1320479
## hsa-miR-182-5p 13.386544 -6.173477 1.9068428
## hsa-miR-125a-3p 2.918606 5.702255 1.9135982
## hsa-miR-574-3p 3.881123 5.627804 1.5118064
## hsa-miR-1246 1161.454061 -5.543254 0.4841454
## hsa-miR-27a-3p 3.104900 5.520852 1.5172155
## hsa-miR-34a-5p 454.895501 5.496695 0.4713569
## hsa-miR-483-3p 7.946179 5.466798 1.1590900
## hsa-miR-146b-5p 168.632116 5.403200 0.4810898
## hsa-miR-5010-3p 3.921794 5.387762 1.4469663
## hsa-let-7d-5p 588.882373 5.316598 0.3830935
## hsa-miR-3934-5p 5.185416 5.178786 1.3348461
## hsa-miR-654-5p 5.891644 4.997846 1.2928424
## hsa-miR-221-5p 4.368518 4.724120 1.7185689
## hsa-miR-155-5p 11097.869650 4.689762 0.3023658
## hsa-miR-28-3p 59.451504 -4.685299 0.4928530
## hsa-let-7i-5p 1348.149768 4.396787 0.3728220
## hsa-miR-577 4.625677 -4.362689 1.2866443
## hsa-miR-371b-5p 3.811814 4.300270 1.4156369
## hsa-miR-200c-3p 16.731190 3.999709 0.7407163
## hsa-miR-98-5p 271.663474 3.674355 0.3692115
## hsa-miR-591 6.943709 3.637626 1.2949258
## hsa-miR-146a-5p 4963.645408 3.567453 0.4239950
## hsa-miR-363-3p 319.076852 3.381946 0.3436155
## hsa-miR-601 6.414515 -3.372557 1.1293441
## hsa-miR-194-5p 42.517210 -3.323784 0.4329041
## hsa-miR-10a-5p 44.111464 3.261432 0.4429706
## hsa-let-7g-5p 2774.856795 3.241588 0.3288241
## hsa-miR-223-3p 159.998728 3.234347 0.4163864
## hsa-miR-1827 6.679916 3.175454 1.0533972
## hsa-let-7f-5p 763.539513 3.114388 0.3637491
## hsa-miR-494-3p 6.840844 -3.053108 1.0902364
## hsa-miR-337-3p 54.247834 2.875717 0.4877725
## hsa-miR-96-5p 34.142701 -2.740837 0.3653291
## hsa-miR-125b-5p 21.478559 -2.710956 0.8135606
## hsa-miR-542-5p 10.002203 2.610852 0.7551828
## hsa-miR-582-5p 32.434430 -2.502524 0.7101236
## hsa-miR-148a-3p 543.177533 2.491279 0.3797552
## hsa-miR-4284 100.234448 -2.415085 0.5077947
## hsa-miR-221-3p 18.166067 2.359329 0.8392596
## hsa-miR-183-5p 36.030068 -2.346714 0.4115839
## hsa-miR-1183 14.842391 -2.308941 0.6181953
## hsa-miR-181a-2-3p 12.501246 2.285039 0.7036593
## hsa-miR-5196-3p+hsa-miR-6732-3p 14.758482 2.195847 0.6849704
## hsa-miR-374b-5p 119.433283 2.189624 0.4593008
## hsa-let-7e-5p 9.166687 2.139527 0.7199022
## hsa-miR-28-5p 61.342319 -2.093236 0.4506749
## hsa-let-7b-5p 108.579356 2.064284 0.2958303
## hsa-miR-23c 12.349246 -1.974785 0.6984797
## hsa-miR-23a-3p 414.360207 1.958638 0.2983817
## stat pvalue padj
## hsa-miR-222-3p 7.825511 5.055984e-15 1.704589e-13
## hsa-miR-503-5p 6.754441 1.433866e-11 3.222784e-10
## hsa-miR-424-5p 4.710959 2.465534e-06 3.232589e-05
## hsa-miR-150-5p 10.684047 1.208836e-26 8.151011e-25
## hsa-miR-1205 5.760619 8.380606e-09 1.521402e-07
## hsa-miR-450a-5p 3.943890 8.017044e-05 7.568090e-04
## hsa-miR-1252-5p 5.650976 1.595395e-08 2.689380e-07
## hsa-miR-182-5p -3.237539 1.205656e-03 7.295765e-03
## hsa-miR-125a-3p 2.979860 2.883802e-03 1.495774e-02
## hsa-miR-574-3p 3.722569 1.972058e-04 1.551353e-03
## hsa-miR-1246 -11.449563 2.363332e-30 2.230985e-28
## hsa-miR-27a-3p 3.638805 2.739056e-04 2.020054e-03
## hsa-miR-34a-5p 11.661430 2.006445e-31 2.367606e-29
## hsa-miR-483-3p 4.716457 2.399872e-06 3.232589e-05
## hsa-miR-146b-5p 11.231166 2.866686e-29 2.255127e-27
## hsa-miR-5010-3p 3.723488 1.964893e-04 1.551353e-03
## hsa-let-7d-5p 13.878070 8.602975e-44 2.030302e-41
## hsa-miR-3934-5p 3.879688 1.045907e-04 9.406923e-04
## hsa-miR-654-5p 3.865781 1.107342e-04 9.678986e-04
## hsa-miR-221-5p 2.748869 5.980134e-03 2.822623e-02
## hsa-miR-155-5p 15.510228 2.958286e-54 1.396311e-51
## hsa-miR-28-3p -9.506484 1.972160e-21 8.462360e-20
## hsa-let-7i-5p 11.793261 4.228466e-32 6.652786e-30
## hsa-miR-577 -3.390750 6.970165e-04 4.569330e-03
## hsa-miR-371b-5p 3.037693 2.383970e-03 1.293372e-02
## hsa-miR-200c-3p 5.399786 6.672047e-08 1.085933e-06
## hsa-miR-98-5p 9.951896 2.474224e-23 1.459792e-21
## hsa-miR-591 2.809139 4.967426e-03 2.417139e-02
## hsa-miR-146a-5p 8.413903 3.965876e-17 1.439918e-15
## hsa-miR-363-3p 9.842240 7.404394e-23 3.494874e-21
## hsa-miR-601 -2.986297 2.823779e-03 1.480915e-02
## hsa-miR-194-5p -7.677875 1.617490e-14 4.771597e-13
## hsa-miR-10a-5p 7.362638 1.803112e-13 4.728159e-12
## hsa-let-7g-5p 9.858123 6.322031e-23 3.315554e-21
## hsa-miR-223-3p 7.767657 7.995091e-15 2.515789e-13
## hsa-miR-1827 3.014488 2.574131e-03 1.365157e-02
## hsa-let-7f-5p 8.561913 1.110102e-17 4.366401e-16
## hsa-miR-494-3p -2.800409 5.103791e-03 2.433323e-02
## hsa-miR-337-3p 5.895612 3.732951e-09 7.047812e-08
## hsa-miR-96-5p -7.502377 6.267074e-14 1.740035e-12
## hsa-miR-125b-5p -3.332211 8.615881e-04 5.495535e-03
## hsa-miR-542-5p 3.457245 5.457287e-04 3.787999e-03
## hsa-miR-582-5p -3.524068 4.249749e-04 3.039215e-03
## hsa-miR-148a-3p 6.560222 5.372773e-11 1.102586e-09
## hsa-miR-4284 -4.756026 1.974410e-06 2.740945e-05
## hsa-miR-221-3p 2.811202 4.935673e-03 2.417139e-02
## hsa-miR-183-5p -5.701666 1.186421e-08 2.074039e-07
## hsa-miR-1183 -3.734971 1.877372e-04 1.527792e-03
## hsa-miR-181a-2-3p 3.247365 1.164787e-03 7.139995e-03
## hsa-miR-5196-3p+hsa-miR-6732-3p 3.205755 1.347086e-03 7.947806e-03
## hsa-miR-374b-5p 4.767298 1.867131e-06 2.670564e-05
## hsa-let-7e-5p 2.971969 2.958965e-03 1.518078e-02
## hsa-miR-28-5p -4.644669 3.406229e-06 4.345243e-05
## hsa-let-7b-5p 6.977935 2.995511e-12 7.069406e-11
## hsa-miR-23c -2.827262 4.694785e-03 2.332567e-02
## hsa-miR-23a-3p 6.564203 5.231203e-11 1.102586e-09
## Kingdom
## hsa-miR-222-3p hsa-miR-222-3p
## hsa-miR-503-5p hsa-miR-503-5p
## hsa-miR-424-5p hsa-miR-424-5p
## hsa-miR-150-5p hsa-miR-150-5p
## hsa-miR-1205 hsa-miR-1205
## hsa-miR-450a-5p hsa-miR-450a-5p
## hsa-miR-1252-5p hsa-miR-1252-5p
## hsa-miR-182-5p hsa-miR-182-5p
## hsa-miR-125a-3p hsa-miR-125a-3p
## hsa-miR-574-3p hsa-miR-574-3p
## hsa-miR-1246 hsa-miR-1246
## hsa-miR-27a-3p hsa-miR-27a-3p
## hsa-miR-34a-5p hsa-miR-34a-5p
## hsa-miR-483-3p hsa-miR-483-3p
## hsa-miR-146b-5p hsa-miR-146b-5p
## hsa-miR-5010-3p hsa-miR-5010-3p
## hsa-let-7d-5p hsa-let-7d-5p
## hsa-miR-3934-5p hsa-miR-3934-5p
## hsa-miR-654-5p hsa-miR-654-5p
## hsa-miR-221-5p hsa-miR-221-5p
## hsa-miR-155-5p hsa-miR-155-5p
## hsa-miR-28-3p hsa-miR-28-3p
## hsa-let-7i-5p hsa-let-7i-5p
## hsa-miR-577 hsa-miR-577
## hsa-miR-371b-5p hsa-miR-371b-5p
## hsa-miR-200c-3p hsa-miR-200c-3p
## hsa-miR-98-5p hsa-miR-98-5p
## hsa-miR-591 hsa-miR-591
## hsa-miR-146a-5p hsa-miR-146a-5p
## hsa-miR-363-3p hsa-miR-363-3p
## hsa-miR-601 hsa-miR-601
## hsa-miR-194-5p hsa-miR-194-5p
## hsa-miR-10a-5p hsa-miR-10a-5p
## hsa-let-7g-5p hsa-let-7g-5p
## hsa-miR-223-3p hsa-miR-223-3p
## hsa-miR-1827 hsa-miR-1827
## hsa-let-7f-5p hsa-let-7f-5p
## hsa-miR-494-3p hsa-miR-494-3p
## hsa-miR-337-3p hsa-miR-337-3p
## hsa-miR-96-5p hsa-miR-96-5p
## hsa-miR-125b-5p hsa-miR-125b-5p
## hsa-miR-542-5p hsa-miR-542-5p
## hsa-miR-582-5p hsa-miR-582-5p
## hsa-miR-148a-3p hsa-miR-148a-3p
## hsa-miR-4284 hsa-miR-4284
## hsa-miR-221-3p hsa-miR-221-3p
## hsa-miR-183-5p hsa-miR-183-5p
## hsa-miR-1183 hsa-miR-1183
## hsa-miR-181a-2-3p hsa-miR-181a-2-3p
## hsa-miR-5196-3p+hsa-miR-6732-3p hsa-miR-5196-3p+hsa-miR-6732-3p
## hsa-miR-374b-5p hsa-miR-374b-5p
## hsa-let-7e-5p hsa-let-7e-5p
## hsa-miR-28-5p hsa-miR-28-5p
## hsa-let-7b-5p hsa-let-7b-5p
## hsa-miR-23c hsa-miR-23c
## hsa-miR-23a-3p hsa-miR-23a-3p
## Phylum
## hsa-miR-222-3p hsa-miR-222-3p
## hsa-miR-503-5p hsa-miR-503-5p
## hsa-miR-424-5p hsa-miR-424-5p
## hsa-miR-150-5p hsa-miR-150-5p
## hsa-miR-1205 hsa-miR-1205
## hsa-miR-450a-5p hsa-miR-450a-5p
## hsa-miR-1252-5p hsa-miR-1252-5p
## hsa-miR-182-5p hsa-miR-182-5p
## hsa-miR-125a-3p hsa-miR-125a-3p
## hsa-miR-574-3p hsa-miR-574-3p
## hsa-miR-1246 hsa-miR-1246
## hsa-miR-27a-3p hsa-miR-27a-3p
## hsa-miR-34a-5p hsa-miR-34a-5p
## hsa-miR-483-3p hsa-miR-483-3p
## hsa-miR-146b-5p hsa-miR-146b-5p
## hsa-miR-5010-3p hsa-miR-5010-3p
## hsa-let-7d-5p hsa-let-7d-5p
## hsa-miR-3934-5p hsa-miR-3934-5p
## hsa-miR-654-5p hsa-miR-654-5p
## hsa-miR-221-5p hsa-miR-221-5p
## hsa-miR-155-5p hsa-miR-155-5p
## hsa-miR-28-3p hsa-miR-28-3p
## hsa-let-7i-5p hsa-let-7i-5p
## hsa-miR-577 hsa-miR-577
## hsa-miR-371b-5p hsa-miR-371b-5p
## hsa-miR-200c-3p hsa-miR-200c-3p
## hsa-miR-98-5p hsa-miR-98-5p
## hsa-miR-591 hsa-miR-591
## hsa-miR-146a-5p hsa-miR-146a-5p
## hsa-miR-363-3p hsa-miR-363-3p
## hsa-miR-601 hsa-miR-601
## hsa-miR-194-5p hsa-miR-194-5p
## hsa-miR-10a-5p hsa-miR-10a-5p
## hsa-let-7g-5p hsa-let-7g-5p
## hsa-miR-223-3p hsa-miR-223-3p
## hsa-miR-1827 hsa-miR-1827
## hsa-let-7f-5p hsa-let-7f-5p
## hsa-miR-494-3p hsa-miR-494-3p
## hsa-miR-337-3p hsa-miR-337-3p
## hsa-miR-96-5p hsa-miR-96-5p
## hsa-miR-125b-5p hsa-miR-125b-5p
## hsa-miR-542-5p hsa-miR-542-5p
## hsa-miR-582-5p hsa-miR-582-5p
## hsa-miR-148a-3p hsa-miR-148a-3p
## hsa-miR-4284 hsa-miR-4284
## hsa-miR-221-3p hsa-miR-221-3p
## hsa-miR-183-5p hsa-miR-183-5p
## hsa-miR-1183 hsa-miR-1183
## hsa-miR-181a-2-3p hsa-miR-181a-2-3p
## hsa-miR-5196-3p+hsa-miR-6732-3p hsa-miR-5196-3p+hsa-miR-6732-3p
## hsa-miR-374b-5p hsa-miR-374b-5p
## hsa-let-7e-5p hsa-let-7e-5p
## hsa-miR-28-5p hsa-miR-28-5p
## hsa-let-7b-5p hsa-let-7b-5p
## hsa-miR-23c hsa-miR-23c
## hsa-miR-23a-3p hsa-miR-23a-3p
## Class
## hsa-miR-222-3p hsa-miR-222-3p
## hsa-miR-503-5p hsa-miR-503-5p
## hsa-miR-424-5p hsa-miR-424-5p
## hsa-miR-150-5p hsa-miR-150-5p
## hsa-miR-1205 hsa-miR-1205
## hsa-miR-450a-5p hsa-miR-450a-5p
## hsa-miR-1252-5p hsa-miR-1252-5p
## hsa-miR-182-5p hsa-miR-182-5p
## hsa-miR-125a-3p hsa-miR-125a-3p
## hsa-miR-574-3p hsa-miR-574-3p
## hsa-miR-1246 hsa-miR-1246
## hsa-miR-27a-3p hsa-miR-27a-3p
## hsa-miR-34a-5p hsa-miR-34a-5p
## hsa-miR-483-3p hsa-miR-483-3p
## hsa-miR-146b-5p hsa-miR-146b-5p
## hsa-miR-5010-3p hsa-miR-5010-3p
## hsa-let-7d-5p hsa-let-7d-5p
## hsa-miR-3934-5p hsa-miR-3934-5p
## hsa-miR-654-5p hsa-miR-654-5p
## hsa-miR-221-5p hsa-miR-221-5p
## hsa-miR-155-5p hsa-miR-155-5p
## hsa-miR-28-3p hsa-miR-28-3p
## hsa-let-7i-5p hsa-let-7i-5p
## hsa-miR-577 hsa-miR-577
## hsa-miR-371b-5p hsa-miR-371b-5p
## hsa-miR-200c-3p hsa-miR-200c-3p
## hsa-miR-98-5p hsa-miR-98-5p
## hsa-miR-591 hsa-miR-591
## hsa-miR-146a-5p hsa-miR-146a-5p
## hsa-miR-363-3p hsa-miR-363-3p
## hsa-miR-601 hsa-miR-601
## hsa-miR-194-5p hsa-miR-194-5p
## hsa-miR-10a-5p hsa-miR-10a-5p
## hsa-let-7g-5p hsa-let-7g-5p
## hsa-miR-223-3p hsa-miR-223-3p
## hsa-miR-1827 hsa-miR-1827
## hsa-let-7f-5p hsa-let-7f-5p
## hsa-miR-494-3p hsa-miR-494-3p
## hsa-miR-337-3p hsa-miR-337-3p
## hsa-miR-96-5p hsa-miR-96-5p
## hsa-miR-125b-5p hsa-miR-125b-5p
## hsa-miR-542-5p hsa-miR-542-5p
## hsa-miR-582-5p hsa-miR-582-5p
## hsa-miR-148a-3p hsa-miR-148a-3p
## hsa-miR-4284 hsa-miR-4284
## hsa-miR-221-3p hsa-miR-221-3p
## hsa-miR-183-5p hsa-miR-183-5p
## hsa-miR-1183 hsa-miR-1183
## hsa-miR-181a-2-3p hsa-miR-181a-2-3p
## hsa-miR-5196-3p+hsa-miR-6732-3p hsa-miR-5196-3p+hsa-miR-6732-3p
## hsa-miR-374b-5p hsa-miR-374b-5p
## hsa-let-7e-5p hsa-let-7e-5p
## hsa-miR-28-5p hsa-miR-28-5p
## hsa-let-7b-5p hsa-let-7b-5p
## hsa-miR-23c hsa-miR-23c
## hsa-miR-23a-3p hsa-miR-23a-3p
## Order
## hsa-miR-222-3p hsa-miR-222-3p
## hsa-miR-503-5p hsa-miR-503-5p
## hsa-miR-424-5p hsa-miR-424-5p
## hsa-miR-150-5p hsa-miR-150-5p
## hsa-miR-1205 hsa-miR-1205
## hsa-miR-450a-5p hsa-miR-450a-5p
## hsa-miR-1252-5p hsa-miR-1252-5p
## hsa-miR-182-5p hsa-miR-182-5p
## hsa-miR-125a-3p hsa-miR-125a-3p
## hsa-miR-574-3p hsa-miR-574-3p
## hsa-miR-1246 hsa-miR-1246
## hsa-miR-27a-3p hsa-miR-27a-3p
## hsa-miR-34a-5p hsa-miR-34a-5p
## hsa-miR-483-3p hsa-miR-483-3p
## hsa-miR-146b-5p hsa-miR-146b-5p
## hsa-miR-5010-3p hsa-miR-5010-3p
## hsa-let-7d-5p hsa-let-7d-5p
## hsa-miR-3934-5p hsa-miR-3934-5p
## hsa-miR-654-5p hsa-miR-654-5p
## hsa-miR-221-5p hsa-miR-221-5p
## hsa-miR-155-5p hsa-miR-155-5p
## hsa-miR-28-3p hsa-miR-28-3p
## hsa-let-7i-5p hsa-let-7i-5p
## hsa-miR-577 hsa-miR-577
## hsa-miR-371b-5p hsa-miR-371b-5p
## hsa-miR-200c-3p hsa-miR-200c-3p
## hsa-miR-98-5p hsa-miR-98-5p
## hsa-miR-591 hsa-miR-591
## hsa-miR-146a-5p hsa-miR-146a-5p
## hsa-miR-363-3p hsa-miR-363-3p
## hsa-miR-601 hsa-miR-601
## hsa-miR-194-5p hsa-miR-194-5p
## hsa-miR-10a-5p hsa-miR-10a-5p
## hsa-let-7g-5p hsa-let-7g-5p
## hsa-miR-223-3p hsa-miR-223-3p
## hsa-miR-1827 hsa-miR-1827
## hsa-let-7f-5p hsa-let-7f-5p
## hsa-miR-494-3p hsa-miR-494-3p
## hsa-miR-337-3p hsa-miR-337-3p
## hsa-miR-96-5p hsa-miR-96-5p
## hsa-miR-125b-5p hsa-miR-125b-5p
## hsa-miR-542-5p hsa-miR-542-5p
## hsa-miR-582-5p hsa-miR-582-5p
## hsa-miR-148a-3p hsa-miR-148a-3p
## hsa-miR-4284 hsa-miR-4284
## hsa-miR-221-3p hsa-miR-221-3p
## hsa-miR-183-5p hsa-miR-183-5p
## hsa-miR-1183 hsa-miR-1183
## hsa-miR-181a-2-3p hsa-miR-181a-2-3p
## hsa-miR-5196-3p+hsa-miR-6732-3p hsa-miR-5196-3p+hsa-miR-6732-3p
## hsa-miR-374b-5p hsa-miR-374b-5p
## hsa-let-7e-5p hsa-let-7e-5p
## hsa-miR-28-5p hsa-miR-28-5p
## hsa-let-7b-5p hsa-let-7b-5p
## hsa-miR-23c hsa-miR-23c
## hsa-miR-23a-3p hsa-miR-23a-3p
## Family
## hsa-miR-222-3p hsa-miR-222-3p
## hsa-miR-503-5p hsa-miR-503-5p
## hsa-miR-424-5p hsa-miR-424-5p
## hsa-miR-150-5p hsa-miR-150-5p
## hsa-miR-1205 hsa-miR-1205
## hsa-miR-450a-5p hsa-miR-450a-5p
## hsa-miR-1252-5p hsa-miR-1252-5p
## hsa-miR-182-5p hsa-miR-182-5p
## hsa-miR-125a-3p hsa-miR-125a-3p
## hsa-miR-574-3p hsa-miR-574-3p
## hsa-miR-1246 hsa-miR-1246
## hsa-miR-27a-3p hsa-miR-27a-3p
## hsa-miR-34a-5p hsa-miR-34a-5p
## hsa-miR-483-3p hsa-miR-483-3p
## hsa-miR-146b-5p hsa-miR-146b-5p
## hsa-miR-5010-3p hsa-miR-5010-3p
## hsa-let-7d-5p hsa-let-7d-5p
## hsa-miR-3934-5p hsa-miR-3934-5p
## hsa-miR-654-5p hsa-miR-654-5p
## hsa-miR-221-5p hsa-miR-221-5p
## hsa-miR-155-5p hsa-miR-155-5p
## hsa-miR-28-3p hsa-miR-28-3p
## hsa-let-7i-5p hsa-let-7i-5p
## hsa-miR-577 hsa-miR-577
## hsa-miR-371b-5p hsa-miR-371b-5p
## hsa-miR-200c-3p hsa-miR-200c-3p
## hsa-miR-98-5p hsa-miR-98-5p
## hsa-miR-591 hsa-miR-591
## hsa-miR-146a-5p hsa-miR-146a-5p
## hsa-miR-363-3p hsa-miR-363-3p
## hsa-miR-601 hsa-miR-601
## hsa-miR-194-5p hsa-miR-194-5p
## hsa-miR-10a-5p hsa-miR-10a-5p
## hsa-let-7g-5p hsa-let-7g-5p
## hsa-miR-223-3p hsa-miR-223-3p
## hsa-miR-1827 hsa-miR-1827
## hsa-let-7f-5p hsa-let-7f-5p
## hsa-miR-494-3p hsa-miR-494-3p
## hsa-miR-337-3p hsa-miR-337-3p
## hsa-miR-96-5p hsa-miR-96-5p
## hsa-miR-125b-5p hsa-miR-125b-5p
## hsa-miR-542-5p hsa-miR-542-5p
## hsa-miR-582-5p hsa-miR-582-5p
## hsa-miR-148a-3p hsa-miR-148a-3p
## hsa-miR-4284 hsa-miR-4284
## hsa-miR-221-3p hsa-miR-221-3p
## hsa-miR-183-5p hsa-miR-183-5p
## hsa-miR-1183 hsa-miR-1183
## hsa-miR-181a-2-3p hsa-miR-181a-2-3p
## hsa-miR-5196-3p+hsa-miR-6732-3p hsa-miR-5196-3p+hsa-miR-6732-3p
## hsa-miR-374b-5p hsa-miR-374b-5p
## hsa-let-7e-5p hsa-let-7e-5p
## hsa-miR-28-5p hsa-miR-28-5p
## hsa-let-7b-5p hsa-let-7b-5p
## hsa-miR-23c hsa-miR-23c
## hsa-miR-23a-3p hsa-miR-23a-3p
## Genus
## hsa-miR-222-3p hsa-miR-222-3p
## hsa-miR-503-5p hsa-miR-503-5p
## hsa-miR-424-5p hsa-miR-424-5p
## hsa-miR-150-5p hsa-miR-150-5p
## hsa-miR-1205 hsa-miR-1205
## hsa-miR-450a-5p hsa-miR-450a-5p
## hsa-miR-1252-5p hsa-miR-1252-5p
## hsa-miR-182-5p hsa-miR-182-5p
## hsa-miR-125a-3p hsa-miR-125a-3p
## hsa-miR-574-3p hsa-miR-574-3p
## hsa-miR-1246 hsa-miR-1246
## hsa-miR-27a-3p hsa-miR-27a-3p
## hsa-miR-34a-5p hsa-miR-34a-5p
## hsa-miR-483-3p hsa-miR-483-3p
## hsa-miR-146b-5p hsa-miR-146b-5p
## hsa-miR-5010-3p hsa-miR-5010-3p
## hsa-let-7d-5p hsa-let-7d-5p
## hsa-miR-3934-5p hsa-miR-3934-5p
## hsa-miR-654-5p hsa-miR-654-5p
## hsa-miR-221-5p hsa-miR-221-5p
## hsa-miR-155-5p hsa-miR-155-5p
## hsa-miR-28-3p hsa-miR-28-3p
## hsa-let-7i-5p hsa-let-7i-5p
## hsa-miR-577 hsa-miR-577
## hsa-miR-371b-5p hsa-miR-371b-5p
## hsa-miR-200c-3p hsa-miR-200c-3p
## hsa-miR-98-5p hsa-miR-98-5p
## hsa-miR-591 hsa-miR-591
## hsa-miR-146a-5p hsa-miR-146a-5p
## hsa-miR-363-3p hsa-miR-363-3p
## hsa-miR-601 hsa-miR-601
## hsa-miR-194-5p hsa-miR-194-5p
## hsa-miR-10a-5p hsa-miR-10a-5p
## hsa-let-7g-5p hsa-let-7g-5p
## hsa-miR-223-3p hsa-miR-223-3p
## hsa-miR-1827 hsa-miR-1827
## hsa-let-7f-5p hsa-let-7f-5p
## hsa-miR-494-3p hsa-miR-494-3p
## hsa-miR-337-3p hsa-miR-337-3p
## hsa-miR-96-5p hsa-miR-96-5p
## hsa-miR-125b-5p hsa-miR-125b-5p
## hsa-miR-542-5p hsa-miR-542-5p
## hsa-miR-582-5p hsa-miR-582-5p
## hsa-miR-148a-3p hsa-miR-148a-3p
## hsa-miR-4284 hsa-miR-4284
## hsa-miR-221-3p hsa-miR-221-3p
## hsa-miR-183-5p hsa-miR-183-5p
## hsa-miR-1183 hsa-miR-1183
## hsa-miR-181a-2-3p hsa-miR-181a-2-3p
## hsa-miR-5196-3p+hsa-miR-6732-3p hsa-miR-5196-3p+hsa-miR-6732-3p
## hsa-miR-374b-5p hsa-miR-374b-5p
## hsa-let-7e-5p hsa-let-7e-5p
## hsa-miR-28-5p hsa-miR-28-5p
## hsa-let-7b-5p hsa-let-7b-5p
## hsa-miR-23c hsa-miR-23c
## hsa-miR-23a-3p hsa-miR-23a-3p
## Species
## hsa-miR-222-3p hsa-miR-222-3p
## hsa-miR-503-5p hsa-miR-503-5p
## hsa-miR-424-5p hsa-miR-424-5p
## hsa-miR-150-5p hsa-miR-150-5p
## hsa-miR-1205 hsa-miR-1205
## hsa-miR-450a-5p hsa-miR-450a-5p
## hsa-miR-1252-5p hsa-miR-1252-5p
## hsa-miR-182-5p hsa-miR-182-5p
## hsa-miR-125a-3p hsa-miR-125a-3p
## hsa-miR-574-3p hsa-miR-574-3p
## hsa-miR-1246 hsa-miR-1246
## hsa-miR-27a-3p hsa-miR-27a-3p
## hsa-miR-34a-5p hsa-miR-34a-5p
## hsa-miR-483-3p hsa-miR-483-3p
## hsa-miR-146b-5p hsa-miR-146b-5p
## hsa-miR-5010-3p hsa-miR-5010-3p
## hsa-let-7d-5p hsa-let-7d-5p
## hsa-miR-3934-5p hsa-miR-3934-5p
## hsa-miR-654-5p hsa-miR-654-5p
## hsa-miR-221-5p hsa-miR-221-5p
## hsa-miR-155-5p hsa-miR-155-5p
## hsa-miR-28-3p hsa-miR-28-3p
## hsa-let-7i-5p hsa-let-7i-5p
## hsa-miR-577 hsa-miR-577
## hsa-miR-371b-5p hsa-miR-371b-5p
## hsa-miR-200c-3p hsa-miR-200c-3p
## hsa-miR-98-5p hsa-miR-98-5p
## hsa-miR-591 hsa-miR-591
## hsa-miR-146a-5p hsa-miR-146a-5p
## hsa-miR-363-3p hsa-miR-363-3p
## hsa-miR-601 hsa-miR-601
## hsa-miR-194-5p hsa-miR-194-5p
## hsa-miR-10a-5p hsa-miR-10a-5p
## hsa-let-7g-5p hsa-let-7g-5p
## hsa-miR-223-3p hsa-miR-223-3p
## hsa-miR-1827 hsa-miR-1827
## hsa-let-7f-5p hsa-let-7f-5p
## hsa-miR-494-3p hsa-miR-494-3p
## hsa-miR-337-3p hsa-miR-337-3p
## hsa-miR-96-5p hsa-miR-96-5p
## hsa-miR-125b-5p hsa-miR-125b-5p
## hsa-miR-542-5p hsa-miR-542-5p
## hsa-miR-582-5p hsa-miR-582-5p
## hsa-miR-148a-3p hsa-miR-148a-3p
## hsa-miR-4284 hsa-miR-4284
## hsa-miR-221-3p hsa-miR-221-3p
## hsa-miR-183-5p hsa-miR-183-5p
## hsa-miR-1183 hsa-miR-1183
## hsa-miR-181a-2-3p hsa-miR-181a-2-3p
## hsa-miR-5196-3p+hsa-miR-6732-3p hsa-miR-5196-3p+hsa-miR-6732-3p
## hsa-miR-374b-5p hsa-miR-374b-5p
## hsa-let-7e-5p hsa-let-7e-5p
## hsa-miR-28-5p hsa-miR-28-5p
## hsa-let-7b-5p hsa-let-7b-5p
## hsa-miR-23c hsa-miR-23c
## hsa-miR-23a-3p hsa-miR-23a-3p########################################################################################################################
ush1.ush2.common_miRNAs <- merge(ush1.nbt.sigtab2, ush2.nbt.sigtab2, by = "row.names", all = T)
ush1.ush3.common_miRNAs <- merge(ush1.nbt.sigtab2,ush3.nbt.sigtab2, by = "row.names", all = T)
ush2.ush3.common_miRNAs <- merge(ush2.nbt.sigtab2,ush3.nbt.sigtab2, by = "row.names", all = T)
row.names(ush1.ush2.common_miRNAs) <- ush1.ush2.common_miRNAs$Row.names
ush1.ush2.ush3.common_miRNAs<- merge(ush1.ush2.common_miRNAs, ush3.nbt.sigtab2, by = "row.names", all = TRUE)## Warning in merge.data.frame(ush1.ush2.common_miRNAs, ush3.nbt.sigtab2, by =
## "row.names", : column name 'Row.names' is duplicated in the resultDESeq2 diff miRNAs by sample type source:
gm_mean = function(x, na.rm=TRUE){
exp(sum(log(x[x > 0]), na.rm=na.rm) / length(x))
}
dds <- phyloseq_to_deseq2(uvc.ps, ~source)## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factorsgeoMeans <- apply(counts(dds), 1, gm_mean)
dds = estimateSizeFactors(dds,geoMeans=geoMeans)
dds = DESeq2::DESeq(dds, test = "Wald", fitType = "local")## using pre-existing size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 1 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingresultsNames(dds)## [1] "Intercept" "source_patient_vs_control"#########################################################################################################################
pat.ctrl.res = results(dds, contrast =c("source","patient","control") )
pat.ctrl.res = pat.ctrl.res[order(pat.ctrl.res$padj, na.last=NA), ]
alpha = 0.05
pat.ctrl.sigtab = pat.ctrl.res[(pat.ctrl.res$padj < alpha), ]
pat.ctrl.sigtab = cbind(as(pat.ctrl.sigtab, "data.frame"), as(tax_table(uvc.ps)[rownames(pat.ctrl.sigtab), ], "matrix"))
#order df by log fold change and manually inspect where 2fold change threshold is, in this case row 76:
o = order(abs(pat.ctrl.sigtab$log2FoldChange), decreasing = TRUE)
pat.ctrl.sigtab2 = pat.ctrl.sigtab[o, ]
pat.ctrl.sigtab2 = pat.ctrl.sigtab2[1:56,]
pat.ctrl.sigtab2## baseMean log2FoldChange lfcSE stat
## hsa-miR-222-3p 62.352476 8.828492 1.0741073 8.219376
## hsa-miR-150-5p 452.972299 8.366903 1.2131767 6.896689
## hsa-miR-424-5p 26.056195 7.568949 1.3567689 5.578657
## hsa-miR-503-5p 22.185375 7.330651 1.0816025 6.777584
## hsa-miR-1252-5p 14.162051 6.685254 1.0933238 6.114615
## hsa-miR-182-5p 13.386544 -6.662012 1.2325499 -5.405065
## hsa-miR-450a-5p 22.279584 6.617966 1.4239369 4.647654
## hsa-miR-1205 11.550695 6.394347 1.0879091 5.877649
## hsa-miR-146b-5p 168.632116 5.453612 0.5079041 10.737483
## hsa-miR-654-5p 5.891644 5.442823 1.1988795 4.539925
## hsa-let-7d-5p 588.882373 5.260088 0.3651535 14.405140
## hsa-miR-3934-5p 5.185416 5.246460 1.2150078 4.318046
## hsa-miR-34a-5p 454.895501 5.230921 0.4166680 12.554171
## hsa-miR-483-3p 7.946179 5.122809 1.1120210 4.606756
## hsa-miR-221-5p 4.368518 4.986916 1.4833170 3.362003
## hsa-miR-5010-3p 3.921794 4.841298 1.3101743 3.695156
## hsa-miR-574-3p 3.881123 4.818546 1.5736489 3.062021
## hsa-miR-129-2-3p 6.247654 -4.724839 1.3871383 -3.406177
## hsa-miR-1246 1161.454061 -4.682589 0.7596397 -6.164224
## hsa-miR-519d-3p 3.246283 4.577166 1.5994772 2.861664
## hsa-miR-27a-3p 3.104900 4.502615 1.4172003 3.177119
## hsa-miR-155-5p 11097.869650 4.488373 0.2846257 15.769391
## hsa-let-7i-5p 1348.149768 4.270705 0.3429287 12.453624
## hsa-miR-591 6.943709 4.200138 1.1826609 3.551431
## hsa-miR-371b-5p 3.811814 4.072891 1.3481598 3.021074
## hsa-miR-28-3p 59.451504 -4.024072 0.4359784 -9.229979
## hsa-miR-503-3p 3.150306 3.784286 1.4263230 2.653176
## hsa-miR-363-3p 319.076852 3.710825 0.3406580 10.893110
## hsa-miR-223-3p 159.998728 3.640410 0.7427580 4.901206
## hsa-miR-1226-3p 4.558990 3.579258 1.2747018 2.807918
## hsa-miR-200c-3p 16.731190 3.521344 0.6964937 5.055816
## hsa-miR-146a-5p 4963.645408 3.399748 0.3736969 9.097608
## hsa-miR-577 4.625677 -3.358210 0.9083915 -3.696875
## hsa-miR-194-5p 42.517210 -3.244839 0.3458610 -9.381915
## hsa-miR-98-5p 271.663474 3.223540 0.4235403 7.610942
## hsa-miR-1827 6.679916 3.213518 1.0058213 3.194919
## hsa-let-7g-5p 2774.856795 3.098503 0.3433172 9.025191
## hsa-miR-513b-5p 4.472696 -3.020751 1.0663190 -2.832878
## hsa-miR-96-5p 34.142701 -2.960385 0.2974687 -9.951921
## hsa-miR-10a-5p 44.111464 2.937573 0.4292533 6.843449
## hsa-let-7f-5p 763.539513 2.844295 0.3150381 9.028417
## hsa-miR-337-3p 54.247834 2.793187 0.4342856 6.431683
## hsa-miR-5196-3p+hsa-miR-6732-3p 14.758482 2.500156 0.5947919 4.203412
## hsa-miR-148a-3p 543.177533 2.438972 0.3292586 7.407466
## hsa-let-7b-5p 108.579356 2.417213 0.3111384 7.768931
## hsa-miR-183-5p 36.030068 -2.407983 0.3356446 -7.174204
## hsa-miR-551b-3p 33.524252 2.366812 0.4273038 5.538943
## hsa-miR-132-3p 128.534206 2.267199 0.4635145 4.891322
## hsa-let-7e-5p 9.166687 2.187612 0.6319375 3.461754
## hsa-miR-28-5p 61.342319 -2.181957 0.3813464 -5.721720
## hsa-miR-221-3p 18.166067 2.145917 0.5719838 3.751710
## hsa-miR-542-5p 10.002203 2.100097 0.7526891 2.790126
## hsa-miR-299-3p 27.522827 -1.999593 0.5034377 -3.971878
## hsa-miR-484 46.995584 -1.964903 0.3030808 -6.483097
## hsa-miR-582-5p 32.434430 -1.936904 0.4886111 -3.964102
## hsa-miR-514a-3p 14.397073 -1.857722 0.4755330 -3.906611
## pvalue padj
## hsa-miR-222-3p 2.045647e-16 8.654660e-15
## hsa-miR-150-5p 5.322838e-12 1.626423e-10
## hsa-miR-424-5p 2.423821e-08 4.761078e-07
## hsa-miR-503-5p 1.222025e-11 3.360568e-10
## hsa-miR-1252-5p 9.679018e-10 2.129384e-08
## hsa-miR-182-5p 6.478500e-08 1.149411e-06
## hsa-miR-450a-5p 3.357318e-06 4.196648e-05
## hsa-miR-1205 4.161351e-09 8.802857e-08
## hsa-miR-146b-5p 6.786941e-27 6.221363e-25
## hsa-miR-654-5p 5.627421e-06 6.728438e-05
## hsa-let-7d-5p 4.803403e-47 1.320936e-44
## hsa-miR-3934-5p 1.574166e-05 1.766921e-04
## hsa-miR-34a-5p 3.771101e-36 6.913685e-34
## hsa-miR-483-3p 4.089996e-06 4.998884e-05
## hsa-miR-221-5p 7.737925e-04 5.319823e-03
## hsa-miR-5010-3p 2.197519e-04 1.803933e-03
## hsa-miR-574-3p 2.198480e-03 1.374050e-02
## hsa-miR-129-2-3p 6.587942e-04 4.705673e-03
## hsa-miR-1246 7.082954e-10 1.623177e-08
## hsa-miR-519d-3p 4.214238e-03 2.439822e-02
## hsa-miR-27a-3p 1.487458e-03 9.624731e-03
## hsa-miR-155-5p 5.053461e-56 2.779403e-53
## hsa-let-7i-5p 1.336363e-35 1.837499e-33
## hsa-miR-591 3.831429e-04 2.886693e-03
## hsa-miR-371b-5p 2.518797e-03 1.522350e-02
## hsa-miR-28-3p 2.706841e-20 1.654181e-18
## hsa-miR-503-3p 7.973823e-03 4.257867e-02
## hsa-miR-363-3p 1.243209e-27 1.367530e-25
## hsa-miR-223-3p 9.525029e-07 1.455213e-05
## hsa-miR-1226-3p 4.986292e-03 2.827279e-02
## hsa-miR-200c-3p 4.285549e-07 6.734433e-06
## hsa-miR-146a-5p 9.234350e-20 5.078893e-18
## hsa-miR-577 2.182696e-04 1.803933e-03
## hsa-miR-194-5p 6.478512e-21 4.453977e-19
## hsa-miR-98-5p 2.721058e-14 9.977213e-13
## hsa-miR-1827 1.398699e-03 9.268488e-03
## hsa-let-7g-5p 1.793819e-19 8.221671e-18
## hsa-miR-513b-5p 4.613102e-03 2.642923e-02
## hsa-miR-96-5p 2.473618e-23 1.943557e-21
## hsa-miR-10a-5p 7.730881e-12 2.237887e-10
## hsa-let-7f-5p 1.741725e-19 8.221671e-18
## hsa-miR-337-3p 1.261984e-10 3.017789e-09
## hsa-miR-5196-3p+hsa-miR-6732-3p 2.629215e-05 2.728430e-04
## hsa-miR-148a-3p 1.287355e-13 4.425281e-12
## hsa-let-7b-5p 7.915111e-15 3.109508e-13
## hsa-miR-183-5p 7.272906e-13 2.352999e-11
## hsa-miR-551b-3p 3.043021e-08 5.771246e-07
## hsa-miR-132-3p 1.001607e-06 1.488876e-05
## hsa-let-7e-5p 5.366681e-04 3.988749e-03
## hsa-miR-28-5p 1.054511e-08 2.148079e-07
## hsa-miR-221-3p 1.756327e-04 1.509343e-03
## hsa-miR-542-5p 5.268753e-03 2.956953e-02
## hsa-miR-299-3p 7.130835e-05 6.761999e-04
## hsa-miR-484 8.985848e-11 2.246462e-09
## hsa-miR-582-5p 7.367260e-05 6.867785e-04
## hsa-miR-514a-3p 9.359970e-05 8.579972e-04
## Kingdom
## hsa-miR-222-3p hsa-miR-222-3p
## hsa-miR-150-5p hsa-miR-150-5p
## hsa-miR-424-5p hsa-miR-424-5p
## hsa-miR-503-5p hsa-miR-503-5p
## hsa-miR-1252-5p hsa-miR-1252-5p
## hsa-miR-182-5p hsa-miR-182-5p
## hsa-miR-450a-5p hsa-miR-450a-5p
## hsa-miR-1205 hsa-miR-1205
## hsa-miR-146b-5p hsa-miR-146b-5p
## hsa-miR-654-5p hsa-miR-654-5p
## hsa-let-7d-5p hsa-let-7d-5p
## hsa-miR-3934-5p hsa-miR-3934-5p
## hsa-miR-34a-5p hsa-miR-34a-5p
## hsa-miR-483-3p hsa-miR-483-3p
## hsa-miR-221-5p hsa-miR-221-5p
## hsa-miR-5010-3p hsa-miR-5010-3p
## hsa-miR-574-3p hsa-miR-574-3p
## hsa-miR-129-2-3p hsa-miR-129-2-3p
## hsa-miR-1246 hsa-miR-1246
## hsa-miR-519d-3p hsa-miR-519d-3p
## hsa-miR-27a-3p hsa-miR-27a-3p
## hsa-miR-155-5p hsa-miR-155-5p
## hsa-let-7i-5p hsa-let-7i-5p
## hsa-miR-591 hsa-miR-591
## hsa-miR-371b-5p hsa-miR-371b-5p
## hsa-miR-28-3p hsa-miR-28-3p
## hsa-miR-503-3p hsa-miR-503-3p
## hsa-miR-363-3p hsa-miR-363-3p
## hsa-miR-223-3p hsa-miR-223-3p
## hsa-miR-1226-3p hsa-miR-1226-3p
## hsa-miR-200c-3p hsa-miR-200c-3p
## hsa-miR-146a-5p hsa-miR-146a-5p
## hsa-miR-577 hsa-miR-577
## hsa-miR-194-5p hsa-miR-194-5p
## hsa-miR-98-5p hsa-miR-98-5p
## hsa-miR-1827 hsa-miR-1827
## hsa-let-7g-5p hsa-let-7g-5p
## hsa-miR-513b-5p hsa-miR-513b-5p
## hsa-miR-96-5p hsa-miR-96-5p
## hsa-miR-10a-5p hsa-miR-10a-5p
## hsa-let-7f-5p hsa-let-7f-5p
## hsa-miR-337-3p hsa-miR-337-3p
## hsa-miR-5196-3p+hsa-miR-6732-3p hsa-miR-5196-3p+hsa-miR-6732-3p
## hsa-miR-148a-3p hsa-miR-148a-3p
## hsa-let-7b-5p hsa-let-7b-5p
## hsa-miR-183-5p hsa-miR-183-5p
## hsa-miR-551b-3p hsa-miR-551b-3p
## hsa-miR-132-3p hsa-miR-132-3p
## hsa-let-7e-5p hsa-let-7e-5p
## hsa-miR-28-5p hsa-miR-28-5p
## hsa-miR-221-3p hsa-miR-221-3p
## hsa-miR-542-5p hsa-miR-542-5p
## hsa-miR-299-3p hsa-miR-299-3p
## hsa-miR-484 hsa-miR-484
## hsa-miR-582-5p hsa-miR-582-5p
## hsa-miR-514a-3p hsa-miR-514a-3p
## Phylum
## hsa-miR-222-3p hsa-miR-222-3p
## hsa-miR-150-5p hsa-miR-150-5p
## hsa-miR-424-5p hsa-miR-424-5p
## hsa-miR-503-5p hsa-miR-503-5p
## hsa-miR-1252-5p hsa-miR-1252-5p
## hsa-miR-182-5p hsa-miR-182-5p
## hsa-miR-450a-5p hsa-miR-450a-5p
## hsa-miR-1205 hsa-miR-1205
## hsa-miR-146b-5p hsa-miR-146b-5p
## hsa-miR-654-5p hsa-miR-654-5p
## hsa-let-7d-5p hsa-let-7d-5p
## hsa-miR-3934-5p hsa-miR-3934-5p
## hsa-miR-34a-5p hsa-miR-34a-5p
## hsa-miR-483-3p hsa-miR-483-3p
## hsa-miR-221-5p hsa-miR-221-5p
## hsa-miR-5010-3p hsa-miR-5010-3p
## hsa-miR-574-3p hsa-miR-574-3p
## hsa-miR-129-2-3p hsa-miR-129-2-3p
## hsa-miR-1246 hsa-miR-1246
## hsa-miR-519d-3p hsa-miR-519d-3p
## hsa-miR-27a-3p hsa-miR-27a-3p
## hsa-miR-155-5p hsa-miR-155-5p
## hsa-let-7i-5p hsa-let-7i-5p
## hsa-miR-591 hsa-miR-591
## hsa-miR-371b-5p hsa-miR-371b-5p
## hsa-miR-28-3p hsa-miR-28-3p
## hsa-miR-503-3p hsa-miR-503-3p
## hsa-miR-363-3p hsa-miR-363-3p
## hsa-miR-223-3p hsa-miR-223-3p
## hsa-miR-1226-3p hsa-miR-1226-3p
## hsa-miR-200c-3p hsa-miR-200c-3p
## hsa-miR-146a-5p hsa-miR-146a-5p
## hsa-miR-577 hsa-miR-577
## hsa-miR-194-5p hsa-miR-194-5p
## hsa-miR-98-5p hsa-miR-98-5p
## hsa-miR-1827 hsa-miR-1827
## hsa-let-7g-5p hsa-let-7g-5p
## hsa-miR-513b-5p hsa-miR-513b-5p
## hsa-miR-96-5p hsa-miR-96-5p
## hsa-miR-10a-5p hsa-miR-10a-5p
## hsa-let-7f-5p hsa-let-7f-5p
## hsa-miR-337-3p hsa-miR-337-3p
## hsa-miR-5196-3p+hsa-miR-6732-3p hsa-miR-5196-3p+hsa-miR-6732-3p
## hsa-miR-148a-3p hsa-miR-148a-3p
## hsa-let-7b-5p hsa-let-7b-5p
## hsa-miR-183-5p hsa-miR-183-5p
## hsa-miR-551b-3p hsa-miR-551b-3p
## hsa-miR-132-3p hsa-miR-132-3p
## hsa-let-7e-5p hsa-let-7e-5p
## hsa-miR-28-5p hsa-miR-28-5p
## hsa-miR-221-3p hsa-miR-221-3p
## hsa-miR-542-5p hsa-miR-542-5p
## hsa-miR-299-3p hsa-miR-299-3p
## hsa-miR-484 hsa-miR-484
## hsa-miR-582-5p hsa-miR-582-5p
## hsa-miR-514a-3p hsa-miR-514a-3p
## Class
## hsa-miR-222-3p hsa-miR-222-3p
## hsa-miR-150-5p hsa-miR-150-5p
## hsa-miR-424-5p hsa-miR-424-5p
## hsa-miR-503-5p hsa-miR-503-5p
## hsa-miR-1252-5p hsa-miR-1252-5p
## hsa-miR-182-5p hsa-miR-182-5p
## hsa-miR-450a-5p hsa-miR-450a-5p
## hsa-miR-1205 hsa-miR-1205
## hsa-miR-146b-5p hsa-miR-146b-5p
## hsa-miR-654-5p hsa-miR-654-5p
## hsa-let-7d-5p hsa-let-7d-5p
## hsa-miR-3934-5p hsa-miR-3934-5p
## hsa-miR-34a-5p hsa-miR-34a-5p
## hsa-miR-483-3p hsa-miR-483-3p
## hsa-miR-221-5p hsa-miR-221-5p
## hsa-miR-5010-3p hsa-miR-5010-3p
## hsa-miR-574-3p hsa-miR-574-3p
## hsa-miR-129-2-3p hsa-miR-129-2-3p
## hsa-miR-1246 hsa-miR-1246
## hsa-miR-519d-3p hsa-miR-519d-3p
## hsa-miR-27a-3p hsa-miR-27a-3p
## hsa-miR-155-5p hsa-miR-155-5p
## hsa-let-7i-5p hsa-let-7i-5p
## hsa-miR-591 hsa-miR-591
## hsa-miR-371b-5p hsa-miR-371b-5p
## hsa-miR-28-3p hsa-miR-28-3p
## hsa-miR-503-3p hsa-miR-503-3p
## hsa-miR-363-3p hsa-miR-363-3p
## hsa-miR-223-3p hsa-miR-223-3p
## hsa-miR-1226-3p hsa-miR-1226-3p
## hsa-miR-200c-3p hsa-miR-200c-3p
## hsa-miR-146a-5p hsa-miR-146a-5p
## hsa-miR-577 hsa-miR-577
## hsa-miR-194-5p hsa-miR-194-5p
## hsa-miR-98-5p hsa-miR-98-5p
## hsa-miR-1827 hsa-miR-1827
## hsa-let-7g-5p hsa-let-7g-5p
## hsa-miR-513b-5p hsa-miR-513b-5p
## hsa-miR-96-5p hsa-miR-96-5p
## hsa-miR-10a-5p hsa-miR-10a-5p
## hsa-let-7f-5p hsa-let-7f-5p
## hsa-miR-337-3p hsa-miR-337-3p
## hsa-miR-5196-3p+hsa-miR-6732-3p hsa-miR-5196-3p+hsa-miR-6732-3p
## hsa-miR-148a-3p hsa-miR-148a-3p
## hsa-let-7b-5p hsa-let-7b-5p
## hsa-miR-183-5p hsa-miR-183-5p
## hsa-miR-551b-3p hsa-miR-551b-3p
## hsa-miR-132-3p hsa-miR-132-3p
## hsa-let-7e-5p hsa-let-7e-5p
## hsa-miR-28-5p hsa-miR-28-5p
## hsa-miR-221-3p hsa-miR-221-3p
## hsa-miR-542-5p hsa-miR-542-5p
## hsa-miR-299-3p hsa-miR-299-3p
## hsa-miR-484 hsa-miR-484
## hsa-miR-582-5p hsa-miR-582-5p
## hsa-miR-514a-3p hsa-miR-514a-3p
## Order
## hsa-miR-222-3p hsa-miR-222-3p
## hsa-miR-150-5p hsa-miR-150-5p
## hsa-miR-424-5p hsa-miR-424-5p
## hsa-miR-503-5p hsa-miR-503-5p
## hsa-miR-1252-5p hsa-miR-1252-5p
## hsa-miR-182-5p hsa-miR-182-5p
## hsa-miR-450a-5p hsa-miR-450a-5p
## hsa-miR-1205 hsa-miR-1205
## hsa-miR-146b-5p hsa-miR-146b-5p
## hsa-miR-654-5p hsa-miR-654-5p
## hsa-let-7d-5p hsa-let-7d-5p
## hsa-miR-3934-5p hsa-miR-3934-5p
## hsa-miR-34a-5p hsa-miR-34a-5p
## hsa-miR-483-3p hsa-miR-483-3p
## hsa-miR-221-5p hsa-miR-221-5p
## hsa-miR-5010-3p hsa-miR-5010-3p
## hsa-miR-574-3p hsa-miR-574-3p
## hsa-miR-129-2-3p hsa-miR-129-2-3p
## hsa-miR-1246 hsa-miR-1246
## hsa-miR-519d-3p hsa-miR-519d-3p
## hsa-miR-27a-3p hsa-miR-27a-3p
## hsa-miR-155-5p hsa-miR-155-5p
## hsa-let-7i-5p hsa-let-7i-5p
## hsa-miR-591 hsa-miR-591
## hsa-miR-371b-5p hsa-miR-371b-5p
## hsa-miR-28-3p hsa-miR-28-3p
## hsa-miR-503-3p hsa-miR-503-3p
## hsa-miR-363-3p hsa-miR-363-3p
## hsa-miR-223-3p hsa-miR-223-3p
## hsa-miR-1226-3p hsa-miR-1226-3p
## hsa-miR-200c-3p hsa-miR-200c-3p
## hsa-miR-146a-5p hsa-miR-146a-5p
## hsa-miR-577 hsa-miR-577
## hsa-miR-194-5p hsa-miR-194-5p
## hsa-miR-98-5p hsa-miR-98-5p
## hsa-miR-1827 hsa-miR-1827
## hsa-let-7g-5p hsa-let-7g-5p
## hsa-miR-513b-5p hsa-miR-513b-5p
## hsa-miR-96-5p hsa-miR-96-5p
## hsa-miR-10a-5p hsa-miR-10a-5p
## hsa-let-7f-5p hsa-let-7f-5p
## hsa-miR-337-3p hsa-miR-337-3p
## hsa-miR-5196-3p+hsa-miR-6732-3p hsa-miR-5196-3p+hsa-miR-6732-3p
## hsa-miR-148a-3p hsa-miR-148a-3p
## hsa-let-7b-5p hsa-let-7b-5p
## hsa-miR-183-5p hsa-miR-183-5p
## hsa-miR-551b-3p hsa-miR-551b-3p
## hsa-miR-132-3p hsa-miR-132-3p
## hsa-let-7e-5p hsa-let-7e-5p
## hsa-miR-28-5p hsa-miR-28-5p
## hsa-miR-221-3p hsa-miR-221-3p
## hsa-miR-542-5p hsa-miR-542-5p
## hsa-miR-299-3p hsa-miR-299-3p
## hsa-miR-484 hsa-miR-484
## hsa-miR-582-5p hsa-miR-582-5p
## hsa-miR-514a-3p hsa-miR-514a-3p
## Family
## hsa-miR-222-3p hsa-miR-222-3p
## hsa-miR-150-5p hsa-miR-150-5p
## hsa-miR-424-5p hsa-miR-424-5p
## hsa-miR-503-5p hsa-miR-503-5p
## hsa-miR-1252-5p hsa-miR-1252-5p
## hsa-miR-182-5p hsa-miR-182-5p
## hsa-miR-450a-5p hsa-miR-450a-5p
## hsa-miR-1205 hsa-miR-1205
## hsa-miR-146b-5p hsa-miR-146b-5p
## hsa-miR-654-5p hsa-miR-654-5p
## hsa-let-7d-5p hsa-let-7d-5p
## hsa-miR-3934-5p hsa-miR-3934-5p
## hsa-miR-34a-5p hsa-miR-34a-5p
## hsa-miR-483-3p hsa-miR-483-3p
## hsa-miR-221-5p hsa-miR-221-5p
## hsa-miR-5010-3p hsa-miR-5010-3p
## hsa-miR-574-3p hsa-miR-574-3p
## hsa-miR-129-2-3p hsa-miR-129-2-3p
## hsa-miR-1246 hsa-miR-1246
## hsa-miR-519d-3p hsa-miR-519d-3p
## hsa-miR-27a-3p hsa-miR-27a-3p
## hsa-miR-155-5p hsa-miR-155-5p
## hsa-let-7i-5p hsa-let-7i-5p
## hsa-miR-591 hsa-miR-591
## hsa-miR-371b-5p hsa-miR-371b-5p
## hsa-miR-28-3p hsa-miR-28-3p
## hsa-miR-503-3p hsa-miR-503-3p
## hsa-miR-363-3p hsa-miR-363-3p
## hsa-miR-223-3p hsa-miR-223-3p
## hsa-miR-1226-3p hsa-miR-1226-3p
## hsa-miR-200c-3p hsa-miR-200c-3p
## hsa-miR-146a-5p hsa-miR-146a-5p
## hsa-miR-577 hsa-miR-577
## hsa-miR-194-5p hsa-miR-194-5p
## hsa-miR-98-5p hsa-miR-98-5p
## hsa-miR-1827 hsa-miR-1827
## hsa-let-7g-5p hsa-let-7g-5p
## hsa-miR-513b-5p hsa-miR-513b-5p
## hsa-miR-96-5p hsa-miR-96-5p
## hsa-miR-10a-5p hsa-miR-10a-5p
## hsa-let-7f-5p hsa-let-7f-5p
## hsa-miR-337-3p hsa-miR-337-3p
## hsa-miR-5196-3p+hsa-miR-6732-3p hsa-miR-5196-3p+hsa-miR-6732-3p
## hsa-miR-148a-3p hsa-miR-148a-3p
## hsa-let-7b-5p hsa-let-7b-5p
## hsa-miR-183-5p hsa-miR-183-5p
## hsa-miR-551b-3p hsa-miR-551b-3p
## hsa-miR-132-3p hsa-miR-132-3p
## hsa-let-7e-5p hsa-let-7e-5p
## hsa-miR-28-5p hsa-miR-28-5p
## hsa-miR-221-3p hsa-miR-221-3p
## hsa-miR-542-5p hsa-miR-542-5p
## hsa-miR-299-3p hsa-miR-299-3p
## hsa-miR-484 hsa-miR-484
## hsa-miR-582-5p hsa-miR-582-5p
## hsa-miR-514a-3p hsa-miR-514a-3p
## Genus
## hsa-miR-222-3p hsa-miR-222-3p
## hsa-miR-150-5p hsa-miR-150-5p
## hsa-miR-424-5p hsa-miR-424-5p
## hsa-miR-503-5p hsa-miR-503-5p
## hsa-miR-1252-5p hsa-miR-1252-5p
## hsa-miR-182-5p hsa-miR-182-5p
## hsa-miR-450a-5p hsa-miR-450a-5p
## hsa-miR-1205 hsa-miR-1205
## hsa-miR-146b-5p hsa-miR-146b-5p
## hsa-miR-654-5p hsa-miR-654-5p
## hsa-let-7d-5p hsa-let-7d-5p
## hsa-miR-3934-5p hsa-miR-3934-5p
## hsa-miR-34a-5p hsa-miR-34a-5p
## hsa-miR-483-3p hsa-miR-483-3p
## hsa-miR-221-5p hsa-miR-221-5p
## hsa-miR-5010-3p hsa-miR-5010-3p
## hsa-miR-574-3p hsa-miR-574-3p
## hsa-miR-129-2-3p hsa-miR-129-2-3p
## hsa-miR-1246 hsa-miR-1246
## hsa-miR-519d-3p hsa-miR-519d-3p
## hsa-miR-27a-3p hsa-miR-27a-3p
## hsa-miR-155-5p hsa-miR-155-5p
## hsa-let-7i-5p hsa-let-7i-5p
## hsa-miR-591 hsa-miR-591
## hsa-miR-371b-5p hsa-miR-371b-5p
## hsa-miR-28-3p hsa-miR-28-3p
## hsa-miR-503-3p hsa-miR-503-3p
## hsa-miR-363-3p hsa-miR-363-3p
## hsa-miR-223-3p hsa-miR-223-3p
## hsa-miR-1226-3p hsa-miR-1226-3p
## hsa-miR-200c-3p hsa-miR-200c-3p
## hsa-miR-146a-5p hsa-miR-146a-5p
## hsa-miR-577 hsa-miR-577
## hsa-miR-194-5p hsa-miR-194-5p
## hsa-miR-98-5p hsa-miR-98-5p
## hsa-miR-1827 hsa-miR-1827
## hsa-let-7g-5p hsa-let-7g-5p
## hsa-miR-513b-5p hsa-miR-513b-5p
## hsa-miR-96-5p hsa-miR-96-5p
## hsa-miR-10a-5p hsa-miR-10a-5p
## hsa-let-7f-5p hsa-let-7f-5p
## hsa-miR-337-3p hsa-miR-337-3p
## hsa-miR-5196-3p+hsa-miR-6732-3p hsa-miR-5196-3p+hsa-miR-6732-3p
## hsa-miR-148a-3p hsa-miR-148a-3p
## hsa-let-7b-5p hsa-let-7b-5p
## hsa-miR-183-5p hsa-miR-183-5p
## hsa-miR-551b-3p hsa-miR-551b-3p
## hsa-miR-132-3p hsa-miR-132-3p
## hsa-let-7e-5p hsa-let-7e-5p
## hsa-miR-28-5p hsa-miR-28-5p
## hsa-miR-221-3p hsa-miR-221-3p
## hsa-miR-542-5p hsa-miR-542-5p
## hsa-miR-299-3p hsa-miR-299-3p
## hsa-miR-484 hsa-miR-484
## hsa-miR-582-5p hsa-miR-582-5p
## hsa-miR-514a-3p hsa-miR-514a-3p
## Species
## hsa-miR-222-3p hsa-miR-222-3p
## hsa-miR-150-5p hsa-miR-150-5p
## hsa-miR-424-5p hsa-miR-424-5p
## hsa-miR-503-5p hsa-miR-503-5p
## hsa-miR-1252-5p hsa-miR-1252-5p
## hsa-miR-182-5p hsa-miR-182-5p
## hsa-miR-450a-5p hsa-miR-450a-5p
## hsa-miR-1205 hsa-miR-1205
## hsa-miR-146b-5p hsa-miR-146b-5p
## hsa-miR-654-5p hsa-miR-654-5p
## hsa-let-7d-5p hsa-let-7d-5p
## hsa-miR-3934-5p hsa-miR-3934-5p
## hsa-miR-34a-5p hsa-miR-34a-5p
## hsa-miR-483-3p hsa-miR-483-3p
## hsa-miR-221-5p hsa-miR-221-5p
## hsa-miR-5010-3p hsa-miR-5010-3p
## hsa-miR-574-3p hsa-miR-574-3p
## hsa-miR-129-2-3p hsa-miR-129-2-3p
## hsa-miR-1246 hsa-miR-1246
## hsa-miR-519d-3p hsa-miR-519d-3p
## hsa-miR-27a-3p hsa-miR-27a-3p
## hsa-miR-155-5p hsa-miR-155-5p
## hsa-let-7i-5p hsa-let-7i-5p
## hsa-miR-591 hsa-miR-591
## hsa-miR-371b-5p hsa-miR-371b-5p
## hsa-miR-28-3p hsa-miR-28-3p
## hsa-miR-503-3p hsa-miR-503-3p
## hsa-miR-363-3p hsa-miR-363-3p
## hsa-miR-223-3p hsa-miR-223-3p
## hsa-miR-1226-3p hsa-miR-1226-3p
## hsa-miR-200c-3p hsa-miR-200c-3p
## hsa-miR-146a-5p hsa-miR-146a-5p
## hsa-miR-577 hsa-miR-577
## hsa-miR-194-5p hsa-miR-194-5p
## hsa-miR-98-5p hsa-miR-98-5p
## hsa-miR-1827 hsa-miR-1827
## hsa-let-7g-5p hsa-let-7g-5p
## hsa-miR-513b-5p hsa-miR-513b-5p
## hsa-miR-96-5p hsa-miR-96-5p
## hsa-miR-10a-5p hsa-miR-10a-5p
## hsa-let-7f-5p hsa-let-7f-5p
## hsa-miR-337-3p hsa-miR-337-3p
## hsa-miR-5196-3p+hsa-miR-6732-3p hsa-miR-5196-3p+hsa-miR-6732-3p
## hsa-miR-148a-3p hsa-miR-148a-3p
## hsa-let-7b-5p hsa-let-7b-5p
## hsa-miR-183-5p hsa-miR-183-5p
## hsa-miR-551b-3p hsa-miR-551b-3p
## hsa-miR-132-3p hsa-miR-132-3p
## hsa-let-7e-5p hsa-let-7e-5p
## hsa-miR-28-5p hsa-miR-28-5p
## hsa-miR-221-3p hsa-miR-221-3p
## hsa-miR-542-5p hsa-miR-542-5p
## hsa-miR-299-3p hsa-miR-299-3p
## hsa-miR-484 hsa-miR-484
## hsa-miR-582-5p hsa-miR-582-5p
## hsa-miR-514a-3p hsa-miR-514a-3pLets spend some time figuring out which DE miRNAs are unique to each phenotype. We did this in a couple ways, one is to create the Venn Diagram which shows how many DE miRNAs are unique to each group and subgroup. Then we extract the unique values all the venn clssifications and make a table of the information.
########################################################################################################################
ush1 <- row.names(ush1.nbt.sigtab2)
ush2 <- row.names(ush2.nbt.sigtab2)
ush3 <- row.names(ush3.nbt.sigtab2)
########################################################################################################################
pat.ctrl<- row.names(pat.ctrl.sigtab2)
########################################################################################################################
library(ggvenn)
ushlist <- list('Usher 1' = ush1, 'Usher 2' = ush2, 'Usher 3' = ush3)
ushvenn <- ggvenn(ushlist) + scale_fill_manual(values =c("#8A9197FF","#709AE1FF","#FED439FF"))## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.ushvenn
# ggsave("ushvenn.pdf", ushvenn, width = 10, height = 10)
########################################################################################################################
########################################################################################################################set.seed(1)
Intersect <- function (x) {
# Multiple set version of intersect
# x is a list
if (length(x) == 1) {
unlist(x)
} else if (length(x) == 2) {
intersect(x[[1]], x[[2]])
} else if (length(x) > 2){
intersect(x[[1]], Intersect(x[-1]))
}
}
Union <- function (x) {
# Multiple set version of union
# x is a list
if (length(x) == 1) {
unlist(x)
} else if (length(x) == 2) {
union(x[[1]], x[[2]])
} else if (length(x) > 2) {
union(x[[1]], Union(x[-1]))
}
}
Setdiff <- function (x, y) {
# Remove the union of the y's from the common x's.
# x and y are lists of characters.
xx <- Intersect(x)
yy <- Union(y)
setdiff(xx, yy)
}
########################################################################################################################
ushlist <- list(A = ush1,
B= ush2,
C= ush3)
shared_ush_differentials <- Intersect(ushlist)
ush1_unique <- Setdiff(ushlist[c("A")], ushlist[c("B","C")])
ush2_unique <- Setdiff(ushlist[c("B")], ushlist[c("A","C")])
ush3_unique <- Setdiff(ushlist[c("C")], ushlist[c("A","B")])
ush12_unique <- Setdiff(ushlist[c("A","B")], ushlist[c("C")])
ush13_unique <- Setdiff(ushlist[c("A","C")], ushlist[c("B")])
ush23_unique <- Setdiff(ushlist[c("B","C")], ushlist[c("A")])
full_derepped_list <- Union(ushlist)
namelist <-c("Common To All","Usher 1 Only","Usher 2 Only", "Usher 3 Only", "Usher 1 and 2", "Usher 1 and 3", "Usher 2 and 3")
miRNAs <- list(shared_ush_differentials,
ush1_unique,
ush2_unique,
ush3_unique,
ush12_unique,
ush13_unique,
ush23_unique)
df <- data.frame(Category = namelist,
Total = c(36,20,14,5,2,5,10),
miRNAs = c("hsa-miR-222-3p,hsa-miR-424-5p,hsa-miR-503-5p,
hsa-miR-1252-5p,hsa-miR-1246,hsa-miR-182-5p,
hsa-miR-1205,hsa-miR-450a-5p,hsa-miR-654-5p,
hsa-miR-3934-5p,hsa-miR-221-5p,hsa-miR-34a-5p,
hsa-miR-150-5p,hsa-let-7d-5p,hsa-miR-483-3p,
hsa-miR-146b-5p,hsa-miR-5010-3p,hsa-miR-591,
hsa-miR-155-5p,hsa-miR-363-3p,hsa-let-7i-5p,
hsa-miR-194-5p,hsa-miR-28-3p,hsa-miR-10a-5p,
hsa-miR-146a-5p,hsa-miR-337-3p,hsa-miR-200c-3p,
hsa-miR-96-5p,hsa-miR-5196-3p+hsa-miR-6732-3p,hsa-let-7f-5p,
hsa-miR-577,hsa-let-7g-5p,hsa-miR-183-5p,
hsa-let-7b-5p,hsa-miR-148a-3p,hsa-miR-98-5p",
"hsa-miR-151a-5p,hsa-miR-129-2-3p,hsa-miR-519d-3p,hsa-miR-4431,hsa-miR-345-5p,hsa-miR-152-3p,
hsa-miR-1260b,hsa-miR-3916,hsa-miR-4455,hsa-miR-3195,hsa-miR-195-5p,hsa-miR-1226-3p,
hsa-miR-151a-3p,hsa-miR-181b-2-3p,hsa-miR-1287-5p,hsa-miR-324-5p,hsa-miR-484,hsa-miR-16-5p,
hsa-miR-331-3p,hsa-miR-26b-5p",
"hsa-miR-513b-5p,hsa-miR-137,hsa-miR-370-3p,hsa-miR-503-3p,hsa-miR-767-5p,hsa-miR-132-3p,hsa-let-7c-5p,
hsa-miR-1304-5p,hsa-miR-514a-3p,hsa-let-7a-5p,hsa-miR-342-3p,hsa-miR-181a-5p,hsa-miR-181c-5p,hsa-miR-1193
",
"hsa-miR-125a-3p,hsa-miR-494-3p,hsa-miR-125b-5p,hsa-miR-4284,hsa-miR-181a-2-3p",
"hsa-miR-551b-3p,hsa-miR-299-3p",
"hsa-miR-371b-5p,hsa-miR-601,hsa-miR-1827,hsa-miR-27a-3p,hsa-miR-582-5p",
"hsa-miR-574-3p,hsa-miR-223-3p,hsa-miR-1183,hsa-miR-28-5p,hsa-miR-542-5p,hsa-let-7e-5p,hsa-miR-23c,
hsa-miR-221-3p,hsa-miR-374b-5p,hsa-miR-23a-3p"))
#write.table(df, file = "ush1.ush2.ush3.common_miRNAs.txt", sep = "\t")
library(kableExtra)##
## Attaching package: 'kableExtra'## The following object is masked from 'package:dplyr':
##
## group_rowstab<- kbl(df, format = "html", booktabs = T) %>%
kable_styling(full_width = F, latex_options = c("scale_down")) %>%
row_spec(0, bold = T) %>%
column_spec(3, width = "30em")
#column_spec(1, bold = T, underline = T) %>%
#column_spec(2, bold = T)
#save_kable(tab, file = "~/Desktop/Usher_miRNA/all_de_mirna_tab.pdf")
tab| Category | Total | miRNAs |
|---|---|---|
| Common To All | 36 | hsa-miR-222-3p,hsa-miR-424-5p,hsa-miR-503-5p, hsa-miR-1252-5p,hsa-miR-1246,hsa-miR-182-5p, hsa-miR-1205,hsa-miR-450a-5p,hsa-miR-654-5p, hsa-miR-3934-5p,hsa-miR-221-5p,hsa-miR-34a-5p, hsa-miR-150-5p,hsa-let-7d-5p,hsa-miR-483-3p, hsa-miR-146b-5p,hsa-miR-5010-3p,hsa-miR-591, hsa-miR-155-5p,hsa-miR-363-3p,hsa-let-7i-5p, hsa-miR-194-5p,hsa-miR-28-3p,hsa-miR-10a-5p, hsa-miR-146a-5p,hsa-miR-337-3p,hsa-miR-200c-3p, hsa-miR-96-5p,hsa-miR-5196-3p+hsa-miR-6732-3p,hsa-let-7f-5p, hsa-miR-577,hsa-let-7g-5p,hsa-miR-183-5p, hsa-let-7b-5p,hsa-miR-148a-3p,hsa-miR-98-5p |
| Usher 1 Only | 20 | hsa-miR-151a-5p,hsa-miR-129-2-3p,hsa-miR-519d-3p,hsa-miR-4431,hsa-miR-345-5p,hsa-miR-152-3p, hsa-miR-1260b,hsa-miR-3916,hsa-miR-4455,hsa-miR-3195,hsa-miR-195-5p,hsa-miR-1226-3p, hsa-miR-151a-3p,hsa-miR-181b-2-3p,hsa-miR-1287-5p,hsa-miR-324-5p,hsa-miR-484,hsa-miR-16-5p, hsa-miR-331-3p,hsa-miR-26b-5p |
| Usher 2 Only | 14 | hsa-miR-513b-5p,hsa-miR-137,hsa-miR-370-3p,hsa-miR-503-3p,hsa-miR-767-5p,hsa-miR-132-3p,hsa-let-7c-5p, hsa-miR-1304-5p,hsa-miR-514a-3p,hsa-let-7a-5p,hsa-miR-342-3p,hsa-miR-181a-5p,hsa-miR-181c-5p,hsa-miR-1193 |
| Usher 3 Only | 5 | hsa-miR-125a-3p,hsa-miR-494-3p,hsa-miR-125b-5p,hsa-miR-4284,hsa-miR-181a-2-3p |
| Usher 1 and 2 | 2 | hsa-miR-551b-3p,hsa-miR-299-3p |
| Usher 1 and 3 | 5 | hsa-miR-371b-5p,hsa-miR-601,hsa-miR-1827,hsa-miR-27a-3p,hsa-miR-582-5p |
| Usher 2 and 3 | 10 | hsa-miR-574-3p,hsa-miR-223-3p,hsa-miR-1183,hsa-miR-28-5p,hsa-miR-542-5p,hsa-let-7e-5p,hsa-miR-23c, hsa-miR-221-3p,hsa-miR-374b-5p,hsa-miR-23a-3p |
Heatmap summarizing DE miRNAs
library(ComplexHeatmap)
library(genefilter)##
## Attaching package: 'genefilter'## The following object is masked from 'package:ComplexHeatmap':
##
## dist2## The following objects are masked from 'package:MatrixGenerics':
##
## rowSds, rowVars## The following objects are masked from 'package:matrixStats':
##
## rowSds, rowVarslibrary(circlize)## ========================================
## circlize version 0.4.14
## CRAN page: https://cran.r-project.org/package=circlize
## Github page: https://github.com/jokergoo/circlize
## Documentation: https://jokergoo.github.io/circlize_book/book/
##
## If you use it in published research, please cite:
## Gu, Z. circlize implements and enhances circular visualization
## in R. Bioinformatics 2014.
##
## This message can be suppressed by:
## suppressPackageStartupMessages(library(circlize))
## ========================================library(dendextend)## Registered S3 method overwritten by 'dendextend':
## method from
## rev.hclust vegan##
## ---------------------
## Welcome to dendextend version 1.15.2
## Type citation('dendextend') for how to cite the package.
##
## Type browseVignettes(package = 'dendextend') for the package vignette.
## The github page is: https://github.com/talgalili/dendextend/
##
## Suggestions and bug-reports can be submitted at: https://github.com/talgalili/dendextend/issues
## You may ask questions at stackoverflow, use the r and dendextend tags:
## https://stackoverflow.com/questions/tagged/dendextend
##
## To suppress this message use: suppressPackageStartupMessages(library(dendextend))
## ---------------------##
## Attaching package: 'dendextend'## The following object is masked from 'package:permute':
##
## shuffle## The following object is masked from 'package:data.table':
##
## set## The following object is masked from 'package:stats':
##
## cutreelibrary(dendsort)
col_fun = colorRamp2(c(-2, 0, 2), c("#D62728FF", "white", "#2CA02CFF"))
mat <- scale(t(ush_vs_cont_expr_counts)) #scale normalizes count data: z = (x — u) / s; x=count u=col_mean s=stddev
mat_trim <- mat[,colnames(mat) %in% full_derepped_list]
mat_trim_common <- mat[,colnames(mat) %in% shared_ush_differentials]
mat_trim_ush1 <- mat[,colnames(mat) %in% ush1_unique]
mat_trim_ush2 <- mat[,colnames(mat) %in% ush2_unique]
mat_trim_ush3 <- mat[,colnames(mat) %in% ush3_unique]
mat_trim_ush12 <- mat[,colnames(mat) %in% ush12_unique]
mat_trim_ush13 <- mat[,colnames(mat) %in% ush13_unique]
mat_trim_ush23 <- mat[,colnames(mat) %in% ush23_unique]
#Heatmap(scale(t(miRNA_final_count_tab)))#this is the same as below...
row_dend = as.dendrogram(hclust(dist(mat_trim)))
#row_dend = color_branches(row_dend, k = 5,col=c("#8A9197FF","#709AE1FF","#C80813FF","#46732EFF","orange"))
col_dend = dendsort(hclust(dist(t(mat_trim))))
#pdf(file = "final_htmap.pdf", width = 12, height = 8, fonts = "Times")
fhp <- Heatmap(mat_trim, name = "Scale",
cluster_rows = row_dend,
row_split = 2,
row_title = c("Control","Usher"),
row_dend_reorder = TRUE,
row_title_rot = 0,
column_km = 6,
column_title = LETTERS[1:6],
col= col_fun,
column_names_rot = 45,
column_names_gp = gpar(fontsize=8))
#dev.off()
fhp
#common heatmap
common_heatmap <- Heatmap(mat_trim_common, name = "Scale",
cluster_rows = row_dend,
row_split = 2,
row_title = c("Control","Usher"),
row_dend_reorder = TRUE,
row_title_rot = 0,
column_km = 6,
column_title = LETTERS[1:6],
col= col_fun,
column_names_rot = 45,
column_names_gp = gpar(fontsize=8))
#pdf(file = "common_htmap.pdf", width = 12, height = 8, fonts = "Times")
common_heatmap
#dev.off()
#ush1_only_htmp
ush1_heatmap <- Heatmap(mat_trim_ush1, name = "Scale",
cluster_rows = row_dend,
row_split = 2,
row_title = c("Control","Usher"),
row_dend_reorder = TRUE,
row_title_rot = 0,
column_km = 6,
column_title = LETTERS[1:6],
col= col_fun,
column_names_rot = 45,
column_names_gp = gpar(fontsize=8))
#pdf(file = "ush1_htmap.pdf", width = 12, height = 8, fonts = "Times")
ush1_heatmap
#dev.off()
#ush2_only_htmp
ush2_heatmap <- Heatmap(mat_trim_ush2, name = "Scale",
cluster_rows = row_dend,
row_split = 2,
row_title = c("Control","Usher"),
row_dend_reorder = TRUE,
row_title_rot = 0,
column_km = 6,
column_title = LETTERS[1:6],
col= col_fun,
column_names_rot = 45,
column_names_gp = gpar(fontsize=8))
#pdf(file = "ush2_htmap.pdf", width = 12, height = 8, fonts = "Times")
ush2_heatmap
#dev.off()
#ush 3 heatmap only has one miRNA
#ush3_heatmap <- Heatmap(mat_trim_ush3, name = "Scale",
#cluster_rows = row_dend,
#row_split = 2,
#row_title = c("Control","Usher"),
#row_dend_reorder = TRUE,
#row_title_rot = 0,
#column_km = 2,
#column_title = LETTERS[1:2],
#col= col_fun,
#column_namerot = 45,
#column_namegp = gpar(fontsize=8))
#pdf(file = "ush3_htmap.pdf", width = 12, height = 8, fonts = "Times")
#ush3_heatmap
#dev.off()
#usher1 and 2 only
#ush12_heatmap <- Heatmap(mat_trim_ush12, name = "Scale",
# cluster_rows = row_dend,
# row_split = 2,
# row_dend_reorder = TRUE,
# row_title_rot = 0,
# column_km = 2,
# column_title = LETTERS[1:2],
# col= col_fun,
# column_namerot = 45,
#column_namegp = gpar(fontsize=8))
#pdf(file = "ush12_htmap.pdf", width = 12, height = 8, fonts = "Times")
#ush12_heatmap
#dev.off()
#usher 1 and 3
#ush13_heatmap <- Heatmap(mat_trim_ush13, name = "Scale",
# cluster_rows = row_dend,
# row_split = 2,
# row_title = c("Control","Usher"),
# row_dend_reorder = TRUE,
# row_title_rot = 0,
# column_km = 2,
# column_title = LETTERS[1:2],
#col= col_fun,
#column_namerot = 45,
#column_namegp = gpar(fontsize=8))
#pdf(file = "ush13_htmap.pdf", width = 12, height = 8, fonts = "Times")
#ush13_heatmap
#dev.off()
#Usher 2 and 3
#ush23_heatmap <- Heatmap(mat_trim_ush23, name = "Scale",
# cluster_rows = row_dend,
# row_split = 2,
# row_title = c("Control","Usher"),
# row_dend_reorder = TRUE,
# row_title_rot = 0,
# column_km = 6,
# column_title = LETTERS[1:6],
#col= col_fun,
#column_namerot = 45,
#column_namegp = gpar(fontsize=8))
#pdf(file = "ush23_htmap.pdf", width = 12, height = 8, fonts = "Times")
#ush23_heatmap
#dev.off()
#the top 22 PCA and DESeq consistent
top.miRNAS<- c("hsa-miR-146a-5p","hsa-miR-155-5p","hsa-miR-16-5p","hsa-miR-29b-3p",
"hsa-miR-19b-3p",
"hsa-miR-4454+hsa-miR-7975",
"hsa-miR-142-3p",
"hsa-miR-96-5p",
"hsa-miR-182-5p",
"hsa-miR-183-5p",
"hsa-miR-181b-2-3p",
"hsa-miR-519d-3p",
"hsa-miR-566",
"hsa-miR-4431",
"hsa-miR-484",
"hsa-miR-331-3p",
"hsa-miR-296-5p",
"hsa-miR-15a-5p",
"hsa-miR-129-2-3p",
"hsa-miR-337-3p",
"hsa-miR-223-3p",
"hsa-miR-21-5p")make some tables of the differential miRNAs
#common_sigtab <-
ush1_only_sigtab <- ush1.nbt.sigtab2[row.names(ush1.nbt.sigtab2) %in% ush1_unique,]
ush1_only_sigtab <- ush1_only_sigtab[,1:6]
ush2_only_sigtab <- ush2.nbt.sigtab2[row.names(ush2.nbt.sigtab2) %in% ush2_unique,]
ush2_only_sigtab <- ush2_only_sigtab[,1:6]
ush3_only_sigtab <- ush3.nbt.sigtab2[row.names(ush3.nbt.sigtab2) %in% ush3_unique,]
ush3_only_sigtab <- ush3_only_sigtab[,1:6]
#pat.ctrl_only_sigtab <- pat.ctrl.sigtab2[row.names(pat.ctrl.sigtab2) %in% pat.ctrl_unique,]
#pat.ctrl_only_sigtab <- pat.ctrl_only_sigtab[,1:6]
#car.ctrl_only_sigtab <- car.ctrl.sigtab2[row.names(car.ctrl.sigtab2) %in% car.ctrl_unique,]
#car.ctrl_only_sigtab <- car.ctrl_only_sigtab[,1:6]
cvp_mirs <- c("hsa-miR-591","hsa-miR-1226-3p","hsa-miR-513b-5p","hsa-miR-10a-5p","hsa-let-7f-5p","hsa-miR-5196-3p+hsa-miR-6732-3p","hsa-miR-183-5p","hsa-miR-551b-3p","hsa-miR-132-3p","hsa-miR-542-5p","hsa-miR-299-3p","hsa-miR-92b-3p","hsa-miR-517c-3p+hsa-miR-519a-3p","hsa-miR-1291","hsa-miR-874-3p","hsa-miR-362-3p","hsa-miR-889-3p","hsa-miR-142-5p","hsa-miR-338-5p","hsa-miR-518b","hsa-miR-1244","hsa-miR-449b-5p","hsa-miR-450a-2-3p","hsa-miR-1183","hsa-miR-608","hsa-miR-211-5p","hsa-miR-2053","hsa-miR-181c-5p","hsa-miR-95-3p","hsa-miR-492","hsa-miR-296-5p","hsa-miR-324-5p")
mat1 <- t(ush_vs_cont_expr_counts)
mat2 <- mat1[,colnames(mat1) %in% cvp_mirs]
write.table(mat2, file = "patient_carrier_miRNA_differential_count_tab.tsv", sep = "\t")
write.table(ush1.nbt.sigtab2, file = "ush1.nbt.sigtab.tsv", sep = "\t")
write.table(ush2.nbt.sigtab2, file = "ush2.nbt.sigtab.tsv", sep = "\t")
write.table(ush3.nbt.sigtab2, file = "ush3.nbt.sigtab.tsv", sep = "\t")
write.table(ush1_only_sigtab, file = "ush1.only.sigtab.tsv", sep = "\t")
write.table(ush2_only_sigtab, file = "ush2.only.sigtab.tsv", sep = "\t")
write.table(ush3_only_sigtab, file = "ush3.only.sigtab.tsv", sep = "\t")
write.table(shared_ush_differentials, file = "common_miRNAlist.tsv", sep = "\t")
write.table(ush12_unique, file = "ush12_miRNAlist.tsv", sep = "\t")
write.table(ush13_unique, file = "ush13_miRNAlist.tsv", sep = "\t")
write.table(ush23_unique, file = "ush23_miRNAlist.tsv", sep = "\t")
write.table(ush1_unique, file = "ush1_miRNAlist.tsv", sep = "\t")
write.table(ush2_unique, file = "ush2_miRNAlist.tsv", sep = "\t")
write.table(ush3_unique, file = "ush3_miRNAlist.tsv", sep = "\t")
mat1 <- t(ush_vs_cont_expr_counts)
mat3 <- mat1[,colnames(mat1) %in% full_derepped_list]
write.table(mat3, file = "all_miRNA_differential_count_tab.tsv", sep = "\t")#need to make a subset of the top candidates of differential miRNAs, make a master heatmap, then create a table with logfold changes and adjusted p-values etc.
#the top 12 PCA and DESeq consistent
top.miRNAS<- c("hsa-miR-146a-5p",
"hsa-miR-155-5p",
"hsa-miR-16-5p",
"hsa-miR-29b-3p",
"hsa-miR-19b-3p",
"hsa-miR-4454+hsa-miR-7975",
"hsa-miR-142-3p",
"hsa-miR-96-5p",
"hsa-miR-182-5p",
"hsa-miR-183-5p",
"hsa-miR-484",
"hsa-miR-331-3p",
"hsa-miR-296-5p",
"hsa-miR-15a-5p",
"hsa-miR-150-5p",
"hsa-let-7a-5p",
"hsa-miR-23a-3p",
"hsa-miR-342-3p",
"hsa-miR-4284",
"hsa-miR-26b-5p",
"hsa-miR-223-3p",
"hsa-miR-21-5p")
topmat <- mat[,colnames(mat) %in% top.miRNAS]
View(topmat)
ush_vs_cont_pheno_rename <- ush_vs_cont_pheno
rownames(ush_vs_cont_pheno_rename) <- with(ush_vs_cont_pheno_rename, paste(sampleID,phenotype,genotype, sep = ":"))
View(ush_vs_cont_pheno_rename)
rownames(topmat) <- rownames(ush_vs_cont_pheno_rename)
top_heatmap <- Heatmap(topmat, name = "Scale",
cluster_rows = T,
row_split = 3,
row_title = c("Control","Usher 2 & 3","Usher 1"),
row_dend_reorder = TRUE,
row_title_rot = 0,
column_km = 2,
column_title = LETTERS[1:2],
col= col_fun,
column_names_rot = 45,
column_names_gp = gpar(fontsize=14))
pdf(file = "top_htmap.pdf", width = 12, height = 8, fonts = "Times")
top_heatmap
dev.off()## quartz_off_screen
## 2#need to make a subset of the top candidates of differential miRNAs, make a master heatmap, then create a table with logfold changes and adjusted p-values etc.
#the 12 miRNAs from ddPCR, and the top PCA miRNAs
top.miRNAS<- c(
"hsa-miR-155-5p",
"hsa-miR-16-5p",
"hsa-miR-19b-3p",
"hsa-miR-4454+hsa-miR-7975",
"hsa-miR-142-3p",
"hsa-miR-96-5p",
"hsa-miR-182-5p",
"hsa-miR-183-5p",
"hsa-miR-363-3p",
"hsa-miR-150-5p",
"hsa-let-7a-5p",
"hsa-miR-28-5p",
"hsa-miR-223-3p")
topmat <- mat[,colnames(mat) %in% top.miRNAS]
View(topmat)
ush_vs_cont_pheno_rename <- ush_vs_cont_pheno
rownames(ush_vs_cont_pheno_rename) <- with(ush_vs_cont_pheno_rename, paste(sampleID,phenotype,genotype, sep = ":"))
View(ush_vs_cont_pheno_rename)
rownames(topmat) <- rownames(ush_vs_cont_pheno_rename)
v <- c("Usher-1D-(rep 1):CDH23","Usher-1D-(rep 2):CDH23","Usher-1D-(rep 3):CDH23","Usher-1B-(rep 1):MYO7A","Usher-1B-(rep 2):MYO7A","Control-(rep 1):Normal","Control-(rep 2):Normal","Control-(rep 3):Normal","Usher-3A-(rep 1):CLRN1","Usher-3A-(rep 2):CLRN1","Usher-3A-(rep 3):CLRN1","Usher-3A-(rep 4):CLRN1","Control-(rep 4):Normal","Usher-2A-(rep 1)::USH2A","Usher-2A-(rep 2)::USH2A","Usher-2A-(rep 3):USH2A","Usher-2A-(rep 4):USH2A")
rownames(topmat) <- v
testrnm <- topmat[c(6:8,13,4:5,1:3,14:17,9:12),]
top_heatmap <- Heatmap(testrnm, name = "Scale",
cluster_rows = T,
#row_split = 4,
#row_title = c("Control","Usher 1","Usher 2","Usher 3"),
row_dend_reorder = TRUE,
row_title_rot = 0,
column_km = 2,
column_title = LETTERS[1:2],
col= col_fun,
column_names_rot = 60,
column_names_gp = gpar(fontsize=14))
#pdf(file = "top_htmap.pdf", width = 12, height = 8, fonts = "Times")
top_heatmap
#dev.off()top_counts <- ush_vs_cont_expr_counts[row.names(ush_vs_cont_expr_counts) %in% top.miRNAS,]
top_ush1 <- ush1.nbt.sigtab[row.names(ush1.nbt.sigtab) %in% top.miRNAS,1:6]
top_ush2 <- ush2.nbt.sigtab[row.names(ush2.nbt.sigtab) %in% top.miRNAS,1:6]
top_ush3 <-ush3.nbt.sigtab[row.names(ush3.nbt.sigtab) %in% top.miRNAS,1:6]
write.table(top_counts, file = "top_counts.tsv", sep = "\t")
write.table(top_ush1, file = "top_ush1.tsv", sep = "\t")
write.table(top_ush2, file = "top_ush2.tsv", sep = "\t")
write.table(top_ush3, file = "top_ush3.tsv", sep = "\t")mut_tab <- read.delim("ush_variant_tab.txt")
colnames(mut_tab) <- c("Cell Line","Phenotype","Chromosome Position","Ref/Alt","Genotype","Classification","ACMG Classification Criteria","HGVS cDot","Sequence Ontology","Gene Name","Gene Inheritance","Associated Conditions")
tab2 <- kbl(mut_tab, format = "html", booktabs = T) %>%
kable_styling(full_width = F, latex_options = c("scale_down")) %>%
row_spec(0, bold = T) %>%
column_spec(12, width = "10em") %>%
column_spec(1, bold = T, underline = T)
tab2| Cell Line | Phenotype | Chromosome Position | Ref/Alt | Genotype | Classification | ACMG Classification Criteria | HGVS cDot | Sequence Ontology | Gene Name | Gene Inheritance | Associated Conditions |
|---|---|---|---|---|---|---|---|---|---|---|---|
| D3739 | Usher-1D | 10:71779316 | G/A | Homozygous | Likely Pathogenic | PM2, PS1, PP3 | NM_022124.6:c.5237G>A | missense | CDH23 | Recessive | Usher syndrome type 1D, CDH23-Related Disorders, Autosomal recessive nonsyndromic hearing loss 12;Pituitary adenoma 5, Rare genetic deafness, Retinal dystrophy, Childhood onset hearing loss, Usher syndrome |
| D3741 | Usher-1B | 11:77181589 | G/T | Homozygous | Likely Pathogenic | PM2, PP2, PS1, PP3 | NM_000260.4:c.2904G>T | missense | MYO7A | Recessive | Rare genetic deafness, Autosomal recessive nonsyndromic hearing loss 2, Usher syndrome type 1B |
| D2880 | Usher-3A | 3:150928107 | A/C | Homozygous | VUS/Conflicting | BS1, PVS1 Strong, PP5 | NM_174878.3:c.528T>G | stop gained | CLRN1 | Recessive | Usher syndrome type 3, Rare genetic deafness, Retinitis pigmentosa 61;Usher syndrome type 3A |
| Coriell(GM09053) | Usher-2A | 1:216190280 | AG/- | Heterozygous | Pathogenic | PM2, PVS1, PP5 | NM_206933.4:c.4338_4339delCT | frameshift | USH2A | Recessive | Usher syndrome type 2A, USH2A-Related Disorders, Retinal dystrophy, Retinitis pigmentosa 39;Usher syndrome type 2A |
| 1:215647526 | T/- | Heterozygous | Pathogenic | PM2, PVS1, PP5 | NM_206933.4:c.14787delA | frameshift | USH2A | Recessive | Retinal dystrophy |
ddPCR results
Read in data
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✔ tibble 3.2.1 ✔ purrr 1.0.1
## ✔ tidyr 1.3.0 ✔ forcats 0.5.1
## ✔ readr 2.1.2## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::between() masks data.table::between()
## ✖ IRanges::collapse() masks dplyr::collapse()
## ✖ Biobase::combine() masks BiocGenerics::combine(), dplyr::combine(), gridExtra::combine()
## ✖ matrixStats::count() masks dplyr::count()
## ✖ IRanges::desc() masks dplyr::desc()
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## ✖ dplyr::filter() masks stats::filter()
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## ✖ BiocGenerics::Position() masks ggplot2::Position(), base::Position()
## ✖ purrr::reduce() masks GenomicRanges::reduce(), IRanges::reduce()
## ✖ S4Vectors::rename() masks dplyr::rename()
## ✖ IRanges::slice() masks dplyr::slice()
## ✖ readr::spec() masks genefilter::spec()
## ✖ purrr::transpose() masks data.table::transpose()library(ggpubr)##
## Attaching package: 'ggpubr'## The following object is masked from 'package:dendextend':
##
## rotate## The following object is masked from 'package:microViz':
##
## stat_chulllibrary(rstatix)##
## Attaching package: 'rstatix'## The following object is masked from 'package:genefilter':
##
## Anova## The following object is masked from 'package:IRanges':
##
## desc## The following object is masked from 'package:stats':
##
## filtermiRNA_ddpcr_down <- read.delim("ddpcr_down.txt", stringsAsFactors=TRUE)
miRNA_ddpcr_up <- read.delim("ddpcr_up.txt", stringsAsFactors=TRUE)
levels(miRNA_ddpcr_down$Sample) <- c("CONTROL","USH1B","USH1D","USH2A","USH3A")
levels(miRNA_ddpcr_down$Sample)## [1] "CONTROL" "USH1B" "USH1D" "USH2A" "USH3A"levels(miRNA_ddpcr_up$Sample) <- c("CONTROL","USH1B","USH1D","USH2A","USH3A")
levels(miRNA_ddpcr_up$Sample)## [1] "CONTROL" "USH1B" "USH1D" "USH2A" "USH3A"ANOVAs with Tukey pairwise comparisons:
363-3p
library(dplyr)
library(ggplot2)
library(multcompView)
library(ggthemes)
library(ggsci)
data_summary <- function(data, varname, groupnames){
require(plyr)
summary_func <- function(x, col){
c(mean = mean(x[[col]], na.rm=TRUE),
sd = sd(x[[col]], na.rm=TRUE))
}
data_sum<-ddply(data, groupnames, .fun=summary_func,
varname)
data_sum <- rename(data_sum, c("mean" = varname))
return(data_sum)
}
# Compute the analysis of variance
res.aov363 <- aov( miRNA_363_3p ~ Sample, data = miRNA_ddpcr_up)
# Summary of the analysis
sum_aov_363 <- data_summary(miRNA_ddpcr_up,varname = "miRNA_363_3p", groupnames = "Sample")## Loading required package: plyr## ------------------------------------------------------------------------------## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)## ------------------------------------------------------------------------------##
## Attaching package: 'plyr'## The following objects are masked from 'package:rstatix':
##
## desc, mutate## The following object is masked from 'package:ggpubr':
##
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##
## compact## The following object is masked from 'package:matrixStats':
##
## count## The following object is masked from 'package:IRanges':
##
## desc## The following object is masked from 'package:S4Vectors':
##
## rename## The following objects are masked from 'package:dplyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarizetukey363 <- TukeyHSD(res.aov363)
summary(res.aov363)## Df Sum Sq Mean Sq F value Pr(>F)
## Sample 4 594204 148551 146.1 <2e-16 ***
## Residuals 51 51842 1017
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1group_lettering363 <- multcompLetters4(res.aov363,tukey363) #
group_lettering363## $Sample
## USH1D USH2A USH1B USH3A CONTROL
## "a" "b" "b" "c" "d"group_lettering363 <- data.frame(group_lettering363$Sample$Letters)
group_lettering363$Sample <- rownames(group_lettering363)
group_lettering363$Sample <- c("USH1D","USH2A","USH1B","USH3A","CONTROL")
sum_aov_363 <- merge(sum_aov_363,group_lettering363, by = "Sample" )
colnames(sum_aov_363)[4] <- "group_lettering"
p363 <- ggplot(sum_aov_363, aes(x=reorder(Sample,+miRNA_363_3p), y=miRNA_363_3p, fill=Sample)) +
geom_bar(stat="identity", color="black",
position=position_dodge(),width = 0.5) +
geom_errorbar(aes(ymin=pmax(miRNA_363_3p-sd,0), ymax=miRNA_363_3p+sd), width=.2,
position=position_dodge(.9)) +
scale_fill_manual("Genotype",values = c("CONTROL"="#374E55FF","USH1B"="#DF8F44FF","USH1D"="#00A1D5FF", "USH2A"="#B24745FF","USH3A"="#79AF97FF"))+
geom_text(aes(label = group_lettering, y = miRNA_363_3p + sd), vjust=-0.4, position=position_dodge(0.9))+
labs(title = "miRNA-363-3p ") +
theme(plot.title = element_text(hjust = 0.5),
legend.position="none",
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
xlab("") +
ylab("miRNA-363-3p copies/ng RNA")+
scale_x_discrete(guide = guide_axis(angle = 45))
p363
#ggsave("USH_miR-155-5p_anova.pdf", plot = p1, device = 'pdf', width = 7, height = 7)150-5p
# Compute the analysis of variance
res.aov150 <- aov( miRNA_150_5p ~ Sample, data = miRNA_ddpcr_up)
# Summary of the analysis
sum_aov_150 <- data_summary(miRNA_ddpcr_up,varname = "miRNA_150_5p", groupnames = "Sample")
tukey150 <- TukeyHSD(res.aov150)
summary(res.aov150)## Df Sum Sq Mean Sq F value Pr(>F)
## Sample 4 17895511 4473878 365.7 <2e-16 ***
## Residuals 51 623914 12234
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1group_lettering150 <- multcompLetters4(res.aov150,tukey150) #
group_lettering150## $Sample
## USH2A USH3A USH1D USH1B CONTROL
## "a" "b" "b" "b" "b"group_lettering150 <- data.frame(group_lettering150$Sample$Letters)
group_lettering150$Sample <- rownames(group_lettering150)
sum_aov_150 <- merge(sum_aov_150,group_lettering150, by = "Sample" )
colnames(sum_aov_150)[4] <- "group_lettering"
p150 <- ggplot(sum_aov_150, aes(x=reorder(Sample,+miRNA_150_5p), y=miRNA_150_5p, fill=Sample)) +
geom_bar(stat="identity", color="black",
position=position_dodge(),width = 0.5) +
geom_errorbar(aes(ymin=pmax(miRNA_150_5p-sd,0), ymax=miRNA_150_5p+sd), width=.2,
position=position_dodge(.9)) +
scale_fill_manual("Genotype",values = c("CONTROL"="#374E55FF","USH1B"="#DF8F44FF","USH1D"="#00A1D5FF", "USH2A"="#B24745FF","USH3A"="#79AF97FF"))+
geom_text(aes(label = group_lettering, y = miRNA_150_5p + sd), vjust=-0.4, position=position_dodge(0.9))+
labs(title = "miRNA-150-5p ") +
theme(plot.title = element_text(hjust = 0.5),
legend.position="none",
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
xlab("") +
ylab("miRNA-150-5p copies/ng RNA")+
scale_x_discrete(guide = guide_axis(angle = 45))
p150
#ggsave("USH_miR-155-5p_anova.pdf", plot = p1, device = 'pdf', width = 7, height = 7)155-5p
# Compute the analysis of variance
res.aov155 <- aov( miRNA_155_5p ~ Sample, data = miRNA_ddpcr_up)
# Summary of the analysis
sum_aov_155 <- data_summary(miRNA_ddpcr_up,varname = "miRNA_155_5p", groupnames = "Sample")
tukey155 <- TukeyHSD(res.aov155)
summary(res.aov155)## Df Sum Sq Mean Sq F value Pr(>F)
## Sample 4 6.954e+10 1.739e+10 493.8 <2e-16 ***
## Residuals 51 1.796e+09 3.521e+07
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1group_lettering155 <- multcompLetters4(res.aov155,tukey155) #
group_lettering155## $Sample
## USH2A USH1B USH1D USH3A CONTROL
## "a" "b" "b" "c" "d"group_lettering155 <- data.frame(group_lettering155$Sample$Letters)
group_lettering155$Sample <- rownames(group_lettering155)
sum_aov_155 <- merge(sum_aov_155,group_lettering155, by = "Sample" )
colnames(sum_aov_155)[4] <- "group_lettering"
p155 <- ggplot(sum_aov_155, aes(x=reorder(Sample,+miRNA_155_5p), y=miRNA_155_5p, fill=Sample)) +
geom_bar(stat="identity", color="black",
position=position_dodge(),width = 0.5) +
geom_errorbar(aes(ymin=pmax(miRNA_155_5p-sd,0), ymax=miRNA_155_5p+sd), width=.2,
position=position_dodge(.9)) +
scale_fill_manual("Genotype",values = c("CONTROL"="#374E55FF","USH1B"="#DF8F44FF","USH1D"="#00A1D5FF", "USH2A"="#B24745FF","USH3A"="#79AF97FF"))+
geom_text(aes(label = group_lettering, y = miRNA_155_5p + sd), vjust=-0.4, position=position_dodge(0.9))+
labs(title = "miRNA-155-5p ") +
theme(plot.title = element_text(hjust = 0.5),
legend.position="none",
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
xlab("") +
ylab("miRNA-155-5p copies/ng RNA")+
scale_x_discrete(guide = guide_axis(angle = 45))
p155
#ggsave("USH_miR-155-5p_anova.pdf", plot = p1, device = 'pdf', width = 7, height = 7)223-3p
# Compute the analysis of variance
res.aov223 <- aov( miRNA_223_3p ~ Sample, data = miRNA_ddpcr_up)
# Summary of the analysis
sum_aov_223 <- data_summary(miRNA_ddpcr_up,varname = "miRNA_223_3p", groupnames = "Sample")
tukey223 <- TukeyHSD(res.aov223)
summary(res.aov223)## Df Sum Sq Mean Sq F value Pr(>F)
## Sample 4 4974630 1243658 168.2 <2e-16 ***
## Residuals 51 377032 7393
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1group_lettering223 <- multcompLetters4(res.aov223,tukey223) #
group_lettering223## $Sample
## USH2A USH1D USH3A CONTROL USH1B
## "a" "b" "b" "c" "c"group_lettering223 <- data.frame(group_lettering223$Sample$Letters)
group_lettering223$Sample <- rownames(group_lettering223)
sum_aov_223 <- merge(sum_aov_223,group_lettering223, by = "Sample" )
colnames(sum_aov_223)[4] <- "group_lettering"
p223 <- ggplot(sum_aov_223, aes(x=reorder(Sample,+miRNA_223_3p), y=miRNA_223_3p, fill=Sample)) +
geom_bar(stat="identity", color="black",
position=position_dodge(),width = 0.5) +
geom_errorbar(aes(ymin=pmax(miRNA_223_3p-sd,0), ymax=miRNA_223_3p+sd), width=.2,
position=position_dodge(.9)) +
scale_fill_manual("Genotype",values = c("CONTROL"="#374E55FF","USH1B"="#DF8F44FF","USH1D"="#00A1D5FF", "USH2A"="#B24745FF","USH3A"="#79AF97FF"))+
geom_text(aes(label = group_lettering, y = miRNA_223_3p + sd), vjust=-0.4, position=position_dodge(0.9))+
labs(title = "miRNA-223-3p ") +
theme(plot.title = element_text(hjust = 0.5),
legend.position="none",
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
xlab("") +
ylab("miRNA-223-3p copies/ng RNA")+
scale_x_discrete(guide = guide_axis(angle = 45))
p223
#ggsave("USH_miR-155-3p_anova.pdf", plot = p1, device = 'pdf', width = 7, height = 7)7a-5p
# Compute the analysis of variance
res.aov7a <- aov( let_7a_5p ~ Sample, data = miRNA_ddpcr_up)
# Summary of the analysis
sum_aov_7a <- data_summary(miRNA_ddpcr_up,varname = "let_7a_5p", groupnames = "Sample")
tukey7a <- TukeyHSD(res.aov7a)
summary(res.aov7a)## Df Sum Sq Mean Sq F value Pr(>F)
## Sample 4 435962143 108990536 248.9 <2e-16 ***
## Residuals 51 22330312 437849
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1group_lettering7a <- multcompLetters4(res.aov7a,tukey7a) #
group_lettering7a## $Sample
## USH2A USH3A USH1D USH1B CONTROL
## "a" "b" "b" "c" "d"group_lettering7a <- data.frame(group_lettering7a$Sample$Letters)
group_lettering7a$Sample <- rownames(group_lettering7a)
sum_aov_7a <- merge(sum_aov_7a,group_lettering7a, by = "Sample" )
colnames(sum_aov_7a)[4] <- "group_lettering"
p7a <- ggplot(sum_aov_7a, aes(x=reorder(Sample,+let_7a_5p), y=let_7a_5p, fill=Sample)) +
geom_bar(stat="identity", color="black",
position=position_dodge(),width = 0.5) +
geom_errorbar(aes(ymin=pmax(let_7a_5p-sd,0), ymax=let_7a_5p+sd), width=.2,
position=position_dodge(.9)) +
scale_fill_manual("Genotype",values = c("CONTROL"="#374E55FF","USH1B"="#DF8F44FF","USH1D"="#00A1D5FF", "USH2A"="#B24745FF","USH3A"="#79AF97FF"))+
geom_text(aes(label = group_lettering, y = let_7a_5p + sd), vjust=-0.4, position=position_dodge(0.9))+
labs(title = "let-7a-5p ") +
theme(plot.title = element_text(hjust = 0.5),
legend.position="none",
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
xlab("") +
ylab("let-7a-5p copies/ng RNA")+
scale_x_discrete(guide = guide_axis(angle = 45))
p7a
#ggsave("USH_miR-155-5p_anova.pdf", plot = p1, device = 'pdf', width = 7, height = 7)142-3p
# Compute the analysis of variance
res.aov142 <- aov( miRNA_142_3p ~ Sample, data = miRNA_ddpcr_up)
# Summary of the analysis
sum_aov_142 <- data_summary(miRNA_ddpcr_up,varname = "miRNA_142_3p", groupnames = "Sample")
tukey142 <- TukeyHSD(res.aov142)
summary(res.aov142)## Df Sum Sq Mean Sq F value Pr(>F)
## Sample 4 2.083e+11 5.209e+10 206.9 <2e-16 ***
## Residuals 51 1.284e+10 2.517e+08
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1group_lettering142 <- multcompLetters4(res.aov142,tukey142) #
group_lettering142## $Sample
## USH2A USH1B USH3A CONTROL USH1D
## "a" "b" "b" "c" "d"group_lettering142 <- data.frame(group_lettering142$Sample$Letters)
group_lettering142$Sample <- rownames(group_lettering142)
sum_aov_142 <- merge(sum_aov_142,group_lettering142, by = "Sample" )
colnames(sum_aov_142)[4] <- "group_lettering"
p142 <- ggplot(sum_aov_142, aes(x=reorder(Sample,+miRNA_142_3p), y=miRNA_142_3p, fill=Sample)) +
geom_bar(stat="identity", color="black",
position=position_dodge(),width = 0.5) +
geom_errorbar(aes(ymin=pmax(miRNA_142_3p-sd,0), ymax=miRNA_142_3p+sd), width=.2,
position=position_dodge(.9)) +
scale_fill_manual("Genotype",values = c("CONTROL"="#374E55FF","USH1B"="#DF8F44FF","USH1D"="#00A1D5FF", "USH2A"="#B24745FF","USH3A"="#79AF97FF"))+
geom_text(aes(label = group_lettering, y = miRNA_142_3p + sd), vjust=-0.4, position=position_dodge(0.9))+
labs(title = "miRNA-142-3p ") +
theme(plot.title = element_text(hjust = 0.5),
legend.position="none",
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
xlab("") +
ylab("miRNA-142-3p copies/ng RNA")+
scale_x_discrete(guide = guide_axis(angle = 45))
p142
#ggsave("USH_miR-155-3p_anova.pdf", plot = p1, device = 'pdf', width = 7, height = 7)library(ggpubr)
total_anovas <- ggarrange(p363,p223,p150,p155,p7a,p142,
nrow = 2,
ncol = 3,
legend = "none",
labels = "AUTO")
total_anovas
#ggsave(total_anovas, filename = "ddpcr_up.pdf", device = "pdf", width = 8, height = 8)Downregulated ddPCR miRNAs
96-5p
# Compute the analysis of variance
res.aov96 <- aov( miRNA_96_5p ~ Sample, data = miRNA_ddpcr_down)
# Summary of the analysis
sum_aov_96 <- data_summary(miRNA_ddpcr_down,varname = "miRNA_96_5p", groupnames = "Sample")
tukey96 <- TukeyHSD(res.aov96)
summary(res.aov96)## Df Sum Sq Mean Sq F value Pr(>F)
## Sample 4 146674 36668 484.7 <2e-16 ***
## Residuals 35 2648 76
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1group_lettering96 <- multcompLetters4(res.aov96,tukey96) #
group_lettering96## $Sample
## CONTROL USH3A USH2A USH1B USH1D
## "a" "b" "b" "b" "b"group_lettering96 <- data.frame(group_lettering96$Sample$Letters)
group_lettering96$Sample <- rownames(group_lettering96)
sum_aov_96 <- merge(sum_aov_96,group_lettering96, by = "Sample" )
colnames(sum_aov_96)[4] <- "group_lettering"
p96 <- ggplot(sum_aov_96, aes(x=reorder(Sample,-miRNA_96_5p), y=miRNA_96_5p, fill=Sample)) +
geom_bar(stat="identity", color="black",
position=position_dodge(),width = 0.5) +
geom_errorbar(aes(ymin=pmax(miRNA_96_5p-sd,0), ymax=miRNA_96_5p+sd), width=.2,
position=position_dodge(.9)) +
scale_fill_manual("Genotype",values = c("CONTROL"="#374E55FF","USH1B"="#DF8F44FF","USH1D"="#00A1D5FF", "USH2A"="#B24745FF","USH3A"="#79AF97FF"))+
geom_text(aes(label = group_lettering, y = miRNA_96_5p + sd), vjust=-0.4, position=position_dodge(0.9))+
labs(title = "miRNA-96-5p ") +
theme(plot.title = element_text(hjust = 0.5),
legend.position="none",
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
xlab("") +
ylab("miRNA-96-5p copies/ng RNA")+
scale_x_discrete(guide = guide_axis(angle = 45))
p96
#ggsave("USH_miR-155-5p_anova.pdf", plot = p1, device = 'pdf', width = 7, height = 7)182-3p
# Compute the analysis of variance
res.aov182 <- aov( miRNA_182_3p ~ Sample, data = miRNA_ddpcr_down)
# Summary of the analysis
sum_aov_182 <- data_summary(miRNA_ddpcr_down,varname = "miRNA_182_3p", groupnames = "Sample")
tukey182 <- TukeyHSD(res.aov182)
summary(res.aov182)## Df Sum Sq Mean Sq F value Pr(>F)
## Sample 4 5545167 1386292 799.5 <2e-16 ***
## Residuals 35 60685 1734
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1group_lettering182 <- multcompLetters4(res.aov182,tukey182) #
group_lettering182## $Sample
## CONTROL USH3A USH2A USH1B USH1D
## "a" "b" "b" "b" "b"group_lettering182 <- data.frame(group_lettering182$Sample$Letters)
group_lettering182$Sample <- rownames(group_lettering182)
sum_aov_182 <- merge(sum_aov_182,group_lettering182, by = "Sample" )
colnames(sum_aov_182)[4] <- "group_lettering"
p182 <- ggplot(sum_aov_182, aes(x=reorder(Sample,-miRNA_182_3p), y=miRNA_182_3p, fill=Sample)) +
geom_bar(stat="identity", color="black",
position=position_dodge(),width = 0.5) +
geom_errorbar(aes(ymin=pmax(miRNA_182_3p-sd,0), ymax=miRNA_182_3p+sd), width=.2,
position=position_dodge(.9)) +
scale_fill_manual("Genotype",values = c("CONTROL"="#374E55FF","USH1B"="#DF8F44FF","USH1D"="#00A1D5FF", "USH2A"="#B24745FF","USH3A"="#79AF97FF"))+
geom_text(aes(label = group_lettering, y = miRNA_182_3p + sd), vjust=-0.4, position=position_dodge(0.9))+
labs(title = "miRNA-182-3p ") +
theme(plot.title = element_text(hjust = 0.5),
legend.position="none",
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
xlab("") +
ylab("miRNA-182-3p copies/ng RNA")+
scale_x_discrete(guide = guide_axis(angle = 45))
p182
#ggsave("USH_miR-155-3p_anova.pdf", plot = p1, device = 'pdf', width = 7, height = 7)183-3p
# Compute the analysis of variance
res.aov183 <- aov( miRNA_183_3p ~ Sample, data = miRNA_ddpcr_down)
# Summary of the analysis
sum_aov_183 <- data_summary(miRNA_ddpcr_down,varname = "miRNA_183_3p", groupnames = "Sample")
tukey183 <- TukeyHSD(res.aov183)
summary(res.aov183)## Df Sum Sq Mean Sq F value Pr(>F)
## Sample 4 1177002 294251 1081 <2e-16 ***
## Residuals 35 9529 272
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1group_lettering183 <- multcompLetters4(res.aov183,tukey183) #
group_lettering183## $Sample
## CONTROL USH3A USH2A USH1B USH1D
## "a" "b" "b" "b" "b"group_lettering183 <- data.frame(group_lettering183$Sample$Letters)
group_lettering183$Sample <- rownames(group_lettering183)
sum_aov_183 <- merge(sum_aov_183,group_lettering183, by = "Sample" )
colnames(sum_aov_183)[4] <- "group_lettering"
p183 <- ggplot(sum_aov_183, aes(x=reorder(Sample,-miRNA_183_3p), y=miRNA_183_3p, fill=Sample)) +
geom_bar(stat="identity", color="black",
position=position_dodge(),width = 0.5) +
geom_errorbar(aes(ymin=pmax(miRNA_183_3p-sd,0), ymax=miRNA_183_3p+sd), width=.2,
position=position_dodge(.9)) +
scale_fill_manual("Genotype",values = c("CONTROL"="#374E55FF","USH1B"="#DF8F44FF","USH1D"="#00A1D5FF", "USH2A"="#B24745FF","USH3A"="#79AF97FF"))+
geom_text(aes(label = group_lettering, y = miRNA_183_3p + sd), vjust=-0.4, position=position_dodge(0.9))+
labs(title = "miRNA-183-3p ") +
theme(plot.title = element_text(hjust = 0.5),
legend.position="none",
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
xlab("") +
ylab("miRNA-183-3p copies/ng RNA")+
scale_x_discrete(guide = guide_axis(angle = 45))
p183
#ggsave("USH_miR-155-3p_anova.pdf", plot = p1, device = 'pdf', width = 7, height = 7)28-5p
# Compute the analysis of variance
res.aov28 <- aov( miRNA_28_5p ~ Sample, data = miRNA_ddpcr_down)
# Summary of the analysis
sum_aov_28 <- data_summary(miRNA_ddpcr_down,varname = "miRNA_28_5p", groupnames = "Sample")
tukey28 <- TukeyHSD(res.aov28)
summary(res.aov28)## Df Sum Sq Mean Sq F value Pr(>F)
## Sample 4 149553 37388 97.11 <2e-16 ***
## Residuals 35 13475 385
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1group_lettering28 <- multcompLetters4(res.aov28,tukey28) #
group_lettering28## $Sample
## CONTROL USH3A USH1B USH2A USH1D
## "a" "b" "b" "b" "b"group_lettering28 <- data.frame(group_lettering28$Sample$Letters)
group_lettering28$Sample <- rownames(group_lettering28)
sum_aov_28 <- merge(sum_aov_28,group_lettering28, by = "Sample" )
colnames(sum_aov_28)[4] <- "group_lettering"
p28 <- ggplot(sum_aov_28, aes(x=reorder(Sample, -miRNA_28_5p), y=miRNA_28_5p, fill=Sample)) +
geom_bar(stat="identity", color="black",
position=position_dodge(),width = 0.5) +
geom_errorbar(aes(ymin=pmax(miRNA_28_5p-sd,0), ymax=miRNA_28_5p+sd), width=.2,
position=position_dodge(.9)) +
scale_fill_manual("Genotype",values = c("CONTROL"="#374E55FF","USH1B"="#DF8F44FF","USH1D"="#00A1D5FF", "USH2A"="#B24745FF","USH3A"="#79AF97FF"))+
geom_text(aes(label = group_lettering, y = miRNA_28_5p + sd), vjust=-0.4, position=position_dodge(0.9))+
labs(title = "miRNA-28-5p ") +
theme(plot.title = element_text(hjust = 0.5),
legend.position="none",
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
xlab("") +
ylab("miRNA-28-5p copies/ng RNA")+
scale_x_discrete(guide = guide_axis(angle = 45))
p28
#ggsave("USH_miR-155-5p_anova.pdf", plot = p1, device = 'pdf', width = 7, height = 7)16-5p
# Compute the analysis of variance
res.aov16 <- aov( miRNA_16_5p ~ Sample, data = miRNA_ddpcr_down)
# Summary of the analysis
sum_aov_16 <- data_summary(miRNA_ddpcr_down,varname = "miRNA_16_5p", groupnames = "Sample")
tukey16 <- TukeyHSD(res.aov16)
summary(res.aov16)## Df Sum Sq Mean Sq F value Pr(>F)
## Sample 4 3.844e+09 960920246 1287 <2e-16 ***
## Residuals 35 2.612e+07 746412
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1group_lettering16 <- multcompLetters4(res.aov16,tukey16) #
group_lettering16## $Sample
## CONTROL USH2A USH1B USH3A USH1D
## "a" "b" "c" "d" "e"group_lettering16 <- data.frame(group_lettering16$Sample$Letters)
group_lettering16$Sample <- rownames(group_lettering16)
sum_aov_16 <- merge(sum_aov_16,group_lettering16, by = "Sample" )
colnames(sum_aov_16)[4] <- "group_lettering"
p16 <- ggplot(sum_aov_16, aes(x=reorder(Sample, -miRNA_16_5p), y=miRNA_16_5p, fill=Sample)) +
geom_bar(stat="identity", color="black",
position=position_dodge(),width = 0.5) +
geom_errorbar(aes(ymin=pmax(miRNA_16_5p-sd,0), ymax=miRNA_16_5p+sd), width=.2,
position=position_dodge(.9)) +
scale_fill_manual("Genotype",values = c("CONTROL"="#374E55FF","USH1B"="#DF8F44FF","USH1D"="#00A1D5FF", "USH2A"="#B24745FF","USH3A"="#79AF97FF"))+
geom_text(aes(label = group_lettering, y = miRNA_16_5p + sd), vjust=-0.4, position=position_dodge(0.9))+
labs(title = "miRNA-16-5p ") +
theme(plot.title = element_text(hjust = 0.5),
legend.position="none",
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
xlab("") +
ylab("miRNA-16-5p copies/ng RNA")+
scale_x_discrete(guide = guide_axis(angle = 45))
p16
#ggsave("USH_miR-155-5p_anova.pdf", plot = p1, device = 'pdf', width = 7, height = 7)19b-3p
# Compute the analysis of variance
res.aov19b <- aov( miRNA_19b_3p ~ Sample, data = miRNA_ddpcr_down)
# Summary of the analysis
sum_aov_19b <- data_summary(miRNA_ddpcr_down,varname = "miRNA_19b_3p", groupnames = "Sample")
tukey19b <- TukeyHSD(res.aov19b)
summary(res.aov19b)## Df Sum Sq Mean Sq F value Pr(>F)
## Sample 4 2.427e+10 6.068e+09 1619 <2e-16 ***
## Residuals 35 1.312e+08 3.748e+06
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1group_lettering19b <- multcompLetters4(res.aov19b,tukey19b) #
group_lettering19b## $Sample
## CONTROL USH2A USH1B USH3A USH1D
## "a" "b" "c" "d" "e"group_lettering19b <- data.frame(group_lettering19b$Sample$Letters)
group_lettering19b$Sample <- rownames(group_lettering19b)
sum_aov_19b <- merge(sum_aov_19b,group_lettering19b, by = "Sample" )
colnames(sum_aov_19b)[4] <- "group_lettering"
p19b <- ggplot(sum_aov_19b, aes(x=reorder(Sample,-miRNA_19b_3p), y=miRNA_19b_3p, fill=Sample)) +
geom_bar(stat="identity", color="black",
position=position_dodge(),width = 0.5) +
geom_errorbar(aes(ymin=pmax(miRNA_19b_3p-sd,0), ymax=miRNA_19b_3p+sd), width=.2,
position=position_dodge(.9)) +
scale_fill_manual("Genotype",values = c("CONTROL"="#374E55FF","USH1B"="#DF8F44FF","USH1D"="#00A1D5FF", "USH2A"="#B24745FF","USH3A"="#79AF97FF"))+
geom_text(aes(label = group_lettering, y = miRNA_19b_3p + sd), vjust=-0.4, position=position_dodge(0.9))+
labs(title = "miRNA-19b-3p ") +
theme(plot.title = element_text(hjust = 0.5),
legend.position="none",
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
xlab("") +
ylab("miRNA-19b-3p copies/ng RNA")+
scale_x_discrete(guide = guide_axis(angle = 45))
p19b
#ggsave("USH_miR-155-3p_anova.pdf", plot = p1, device = 'pdf', width = 7, height = 7)combined
down_anovas <- ggarrange(p28,p96,p182,p183,p16,p19b,
nrow = 2,
ncol = 3,
legend = "none",
labels = "AUTO")
down_anovas
#ggsave(down_anovas, filename = "ddpcr_down.pdf", device = "pdf", width = 8, height = 8)library(ampvis2)
#uvc.amp <- phyloseq_to_ampvis2(uvc.ps) ##outdated
uvc.amp <- amp_load(uvc.ps)## Phyloseq object provided. Ignoring anything provided for the metadata, taxonomy, fasta, or tree arguments, using those from the phyloseq object instead.## Warning: Could not find a column named OTU/ASV in otutable, using rownames as
## OTU ID's## Warning: Could not find a column named OTU/ASV in taxonomy, using rownames as
## OTU ID's## 116 OTUs with 0 total abundance across all samples have been removed.prop_mirna_abs <- amp_heatmap(data = uvc.amp, tax_aggregate = "Species", group_by = "genotype", tax_show = 20)
bar_plot <- uvc.ps %>%
tax_fix(min_length = 0,
unknowns = NA,
anon_unique = T) %>%
comp_barplot(
tax_level = "Species", n_taxa = 20, other_name = "Other",
taxon_renamer = function(x) stringr::str_remove(x, " [ae]t rel."),
palette = distinct_palette(n = 20, add = "grey90"),
merge_other = FALSE, bar_outline_colour = "darkgrey"
)## Registered S3 method overwritten by 'seriation':
## method from
## reorder.hclust veganbar_plot
#ggsave(prop_mirna_abs, filename = "mirna_props.pdf", device = "pdf", width = 10, height = 10)
#ggsave(bar_plot, filename = "mirna_bars.pdf", device = "pdf", width = 10, height = 10)fc_tab <- read.delim("fc_tab.txt")
colnames(fc_tab) <- c("microRNA","log2FC","P-Value (BH-adj.)","log2FC","P-Value (BH-adj.)","log2FC", "P-Value (BH-adj.)","Average log2FC","Standard Dev.")
#library(kableExtra)
fctab<- kbl(fc_tab, format = "html", booktabs = T) %>%
kable_styling(full_width = F, latex_options = c("scale_down")) %>%
add_header_above(c(" ", "Usher 1" = 2, "Usher 2" = 2, "Usher 3" = 2,"Mean Log2FC vs. Control"=2), bold = T) %>%
row_spec(0, bold = T) %>%
#column_spec(3, width = "30em") #%>%
column_spec(1, bold = T) #%>%
#column_spec(2, bold = T)
fctab| microRNA | log2FC | P-Value (BH-adj.) | log2FC | P-Value (BH-adj.) | log2FC | P-Value (BH-adj.) | Average log2FC | Standard Dev. |
|---|---|---|---|---|---|---|---|---|
| hsa-miR-150-5p | 4.8901 | 0.0000 | 9.7786 | 0.0000 | 6.5893 | 0.0000 | 7.0860 | 2.4818 |
| hsa-miR-155-5p | 4.1500 | 0.0000 | 4.6412 | 0.0000 | 4.6898 | 0.0000 | 4.4936 | 0.2986 |
| hsa-miR-363-3p | 4.1282 | 0.0000 | 3.3498 | 0.0000 | 3.3819 | 0.0000 | 3.6200 | 0.4404 |
| hsa-miR-223-3p | 1.6878 | 0.0002 | 4.8040 | 0.0000 | 3.2343 | 0.0000 | 3.2420 | 1.5581 |
| hsa-let-7a-5p | 0.9413 | 0.0095 | 2.0768 | 0.0000 | 1.9006 | 0.0000 | 1.6396 | 0.6111 |
| hsa-miR-142-3p | 0.5645 | 0.0259 | 1.6779 | 0.0000 | 1.5316 | 0.0000 | 1.2580 | 0.6051 |
| hsa-miR-19b-3p | -1.4389 | 0.0001 | -0.9554 | 0.0293 | -1.1786 | 0.0041 | -1.1910 | 0.2419 |
| hsa-miR-28-5p | -1.9993 | 0.0000 | -2.5731 | 0.0000 | -2.0932 | 0.0000 | -2.2219 | 0.3078 |
| hsa-miR-16-5p | -2.1845 | 0.0000 | -1.0125 | 0.0487 | -1.3422 | 0.0044 | -1.5130 | 0.6044 |
| hsa-miR-183-5p | -2.2771 | 0.0000 | -2.6795 | 0.0000 | -2.3467 | 0.0000 | -2.4344 | 0.2151 |
| hsa-miR-96-5p | -2.9386 | 0.0000 | -3.2701 | 0.0000 | -2.7408 | 0.0000 | -2.9832 | 0.2674 |
| hsa-miR-182-5p | -6.2691 | 0.0024 | -8.0376 | 0.0005 | -6.1735 | 0.0073 | -6.8267 | 1.0498 |
#save_kable(fctab, file = "mirna_fc.pdf")
ddpcr_fc_tab <- read.delim("ddPCR_log2FC_tab.txt", row.names = 1)
colnames(ddpcr_fc_tab) <- c("log2FC","P-value (Tukey)", "log2FC","P-value (Tukey)", "log2FC","P-value (Tukey)", "log2FC","P-value (Tukey)", "Average log2FC" ,"Standard Dev.")
ddpcr_fc_tab <- kbl(ddpcr_fc_tab, format = "html", booktabs = T) %>%
kable_styling(full_width = F, latex_options = c("scale_down")) %>%
add_header_above(c(" ", "USH1B" = 2,"USH1D"=2,"USH2A"=2,"USH3A"=2, "Summary" = 2), bold = T) %>%
row_spec(0, bold = T) %>%
#column_spec(4, width = "30em") %>%
column_spec(1, bold = T) #%>%
#column_spec(2, bold = T)
ddpcr_fc_tab| log2FC | P-value (Tukey) | log2FC | P-value (Tukey) | log2FC | P-value (Tukey) | log2FC | P-value (Tukey) | Average log2FC | Standard Dev. | |
|---|---|---|---|---|---|---|---|---|---|---|
| miRNA-96-5p | -7.8315 | 0.0000 | -8.4164 | 0.0000 | -6.4778 | 0 | -4.4392 | 0.0000 | -6.7912 | 1.7658 |
| miRNA-182-3p | -7.0483 | 0.0000 | -9.4163 | 0.0000 | -6.2993 | 0 | -4.1755 | 0.0000 | -6.7348 | 2.1624 |
| miRNA-183-3p | -6.4993 | 0.0000 | -8.6036 | 0.0000 | -5.7894 | 0 | -4.2602 | 0.0000 | -6.2881 | 1.8044 |
| miRNA-28-5p | -3.7054 | 0.0000 | -4.3792 | 0.0000 | -4.0710 | 0 | -3.5236 | 0.0000 | -3.9198 | 0.3816 |
| miRNA-19b-3p | -1.8154 | 0.0000 | -2.4705 | 0.0000 | -1.0759 | 0 | -2.1000 | 0.0000 | -1.8655 | 0.5907 |
| miRNA-16-5p | -1.6082 | 0.0000 | -2.1104 | 0.0000 | -0.6188 | 0 | -1.8684 | 0.0000 | -1.5514 | 0.6547 |
| miRNA-223-3p | -0.0316 | 1.0000 | 1.0612 | 0.0013 | 2.9325 | 0 | 1.0534 | 0.0015 | 1.2539 | 1.2312 |
| let-7a-5p | 0.4492 | 0.0001 | 0.8932 | 0.0000 | 1.7187 | 0 | 1.0197 | 0.0000 | 1.0202 | 0.5260 |
| miRNA-142-3p | 0.5956 | 0.0000 | -0.3789 | 0.0444 | 1.6618 | 0 | 0.5381 | 0.0000 | 0.6042 | 0.8345 |
| miRNA-150-5p | 2.7182 | 0.8725 | 2.8787 | 0.8105 | 7.7341 | 0 | 3.5134 | 0.3914 | 4.2111 | 2.3736 |
| miRNA-155-5p | 4.3887 | 0.0000 | 4.3706 | 0.0000 | 4.9500 | 0 | 4.0281 | 0.0000 | 4.4343 | 0.3817 |
| miRNA-363-3p | 4.5878 | 0.0000 | 5.0891 | 0.0000 | 4.7358 | 0 | 3.4480 | 0.0000 | 4.4652 | 0.7100 |
#save_kable(ddpcr_fc_tab, file = "~/Desktop/ddpcr_fc_tab.pdf")Microarray ANOVAS with tukey adjustments for multiple comparisons
make a data tabel of the 12 miRNAs of interest
microarray_counts <- ush_vs_cont_expr_counts[rownames(ush_vs_cont_expr_counts) %in% c("hsa-miR-155-5p","hsa-miR-150-5p","hsa-miR-363-3p","hsa-miR-223-3p","hsa-miR-28-5p","hsa-miR-182-5p","hsa-miR-183-5p","hsa-miR-96-5p","hsa-let-7a-5p","hsa-miR-142-3p","hsa-miR-16-5p","hsa-miR-19b-3p"), ]
microarray_counts <- data.frame(t(microarray_counts))
microarray_counts$sampleID <- rownames(microarray_counts)
newdf <- merge(microarray_counts, ush_vs_cont_pheno_rename, by = "sampleID")
newdf$genotype <- as.factor(newdf$genotype)
levels(newdf$genotype) <- c("USH1D","USH3A","USH1B","CONTROL","USH2A")
levels(newdf$genotype)## [1] "USH1D" "USH3A" "USH1B" "CONTROL" "USH2A"28-5p Microarray
# Compute the analysis of variance
res.aov28.ma <- aov( hsa.miR.28.5p ~ genotype, data = newdf)
# Summary of the analysis
sum_aov_28.ma <- data_summary(newdf,varname = "hsa.miR.28.5p", groupnames = "genotype")
tukey28.ma <- TukeyHSD(res.aov28.ma)
summary(res.aov28.ma)## Df Sum Sq Mean Sq F value Pr(>F)
## genotype 4 41882 10471 6.761 0.00435 **
## Residuals 12 18584 1549
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1group_lettering28.ma <- multcompLetters4(res.aov28.ma,tukey28.ma) #
group_lettering28.ma## $genotype
## CONTROL USH1D USH1B USH3A USH2A
## "a" "b" "b" "b" "b"group_lettering28.ma <- data.frame(group_lettering28.ma$genotype$Letters)
group_lettering28.ma$genotype <- rownames(group_lettering28.ma)
sum_aov_28.ma <- merge(sum_aov_28.ma,group_lettering28.ma, by = "genotype" )
colnames(sum_aov_28.ma)[4] <- "group_lettering"
p28.ma <- ggplot(sum_aov_28.ma, aes(x=reorder(genotype,-hsa.miR.28.5p), y=hsa.miR.28.5p, fill=genotype)) +
geom_bar(stat="identity", color="black",
position=position_dodge(),width = 0.5) +
geom_errorbar(aes(ymin=pmax(hsa.miR.28.5p-sd,0), ymax=hsa.miR.28.5p+sd), width=.2,
position=position_dodge(.9)) +
scale_fill_manual("Genotype",values = c("CONTROL"="#374E55FF","USH1B"="#DF8F44FF","USH1D"="#00A1D5FF", "USH2A"="#B24745FF","USH3A"="#79AF97FF"))+
geom_text(aes(label = group_lettering, y = hsa.miR.28.5p + sd), vjust=-0.4, position=position_dodge(0.9))+
labs(title = "miRNA-28-5p") +
theme(plot.title = element_text(hjust = 0.5),
legend.position="none",
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
xlab("") +
ylab("miRNA-28-5p Count")
p28.ma
19b-3p Microarray
# Compute the analysis of variance
res.aov19b.ma <- aov( hsa.miR.19b.3p ~ genotype, data = newdf)
# Summary of the analysis
sum_aov_19b.ma <- data_summary(newdf,varname = "hsa.miR.19b.3p", groupnames = "genotype")
tukey19b.ma <- TukeyHSD(res.aov19b.ma)
summary(res.aov19b.ma)## Df Sum Sq Mean Sq F value Pr(>F)
## genotype 4 100990175 25247544 28.81 0.00000453 ***
## Residuals 12 10516459 876372
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1group_lettering19b.ma <- multcompLetters4(res.aov19b.ma,tukey19b.ma) #
group_lettering19b.ma## $genotype
## CONTROL USH1B USH2A USH3A USH1D
## "a" "b" "bc" "bc" "c"group_lettering19b.ma <- data.frame(group_lettering19b.ma$genotype$Letters)
group_lettering19b.ma$genotype <- rownames(group_lettering19b.ma)
sum_aov_19b.ma <- merge(sum_aov_19b.ma,group_lettering19b.ma, by = "genotype" )
colnames(sum_aov_19b.ma)[4] <- "group_lettering"
p19b.ma <- ggplot(sum_aov_19b.ma, aes(x=reorder(genotype,-hsa.miR.19b.3p), y=hsa.miR.19b.3p, fill=genotype)) +
geom_bar(stat="identity", color="black",
position=position_dodge(),width = 0.5) +
geom_errorbar(aes(ymin=pmax(hsa.miR.19b.3p-sd,0), ymax=hsa.miR.19b.3p+sd), width=.2,
position=position_dodge(.9)) +
scale_fill_manual("Genotype",values = c("CONTROL"="#374E55FF","USH1B"="#DF8F44FF","USH1D"="#00A1D5FF", "USH2A"="#B24745FF","USH3A"="#79AF97FF"))+
geom_text(aes(label = group_lettering, y = hsa.miR.19b.3p + sd), vjust=-0.4, position=position_dodge(0.9))+
labs(title = "miRNA-19b-3p ") +
theme(plot.title = element_text(hjust = 0.5),
legend.position="none",
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
xlab("") +
ylab("miRNA-19b-3p Count")
p19b.ma
16-5p Microarray
# Compute the analysis of variance
res.aov16.ma <- aov( hsa.miR.16.5p ~ genotype, data = newdf)
# Summary of the analysis
sum_aov_16.ma <- data_summary(newdf,varname = "hsa.miR.16.5p", groupnames = "genotype")
tukey16.ma <- TukeyHSD(res.aov16.ma)
summary(res.aov16.ma)## Df Sum Sq Mean Sq F value Pr(>F)
## genotype 4 560595898 140148975 49.68 0.000000227 ***
## Residuals 12 33851934 2820994
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1group_lettering16.ma <- multcompLetters4(res.aov16.ma,tukey16.ma) #
group_lettering16.ma## $genotype
## CONTROL USH2A USH3A USH1B USH1D
## "a" "b" "b" "bc" "c"group_lettering16.ma <- data.frame(group_lettering16.ma$genotype$Letters)
group_lettering16.ma$genotype <- rownames(group_lettering16.ma)
sum_aov_16.ma <- merge(sum_aov_16.ma,group_lettering16.ma, by = "genotype" )
colnames(sum_aov_16.ma)[4] <- "group_lettering"
p16.ma <- ggplot(sum_aov_16.ma, aes(x=reorder(genotype, -hsa.miR.16.5p), y=hsa.miR.16.5p, fill=genotype)) +
geom_bar(stat="identity", color="black",
position=position_dodge(),width = 0.5) +
geom_errorbar(aes(ymin=pmax(hsa.miR.16.5p-sd,0), ymax=hsa.miR.16.5p+sd), width=.2,
position=position_dodge(.9)) +
scale_fill_manual("Genotype",values = c("CONTROL"="#374E55FF","USH1B"="#DF8F44FF","USH1D"="#00A1D5FF", "USH2A"="#B24745FF","USH3A"="#79AF97FF"))+
geom_text(aes(label = group_lettering, y = hsa.miR.16.5p + sd), vjust=-0.4, position=position_dodge(0.9))+
labs(title = "miRNA-16-5p") +
theme(plot.title = element_text(hjust = 0.5),
legend.position="none",
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
xlab("") +
ylab("miRNA-16-5p Count")
p16.ma
96-5p Microarray
# Compute the analysis of variance
res.aov96.ma <- aov( hsa.miR.96.5p ~ genotype, data = newdf)
# Summary of the analysis
sum_aov_96.ma <- data_summary(newdf,varname = "hsa.miR.96.5p", groupnames = "genotype")
tukey96.ma <- TukeyHSD(res.aov96.ma)
summary(res.aov96.ma)## Df Sum Sq Mean Sq F value Pr(>F)
## genotype 4 26156 6539 28.99 0.00000438 ***
## Residuals 12 2707 226
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1group_lettering96.ma <- multcompLetters4(res.aov96.ma,tukey96.ma) #
group_lettering96.ma## $genotype
## CONTROL USH1D USH3A USH1B USH2A
## "a" "b" "b" "b" "b"group_lettering96.ma <- data.frame(group_lettering96.ma$genotype$Letters)
group_lettering96.ma$genotype <- rownames(group_lettering96.ma)
sum_aov_96.ma <- merge(sum_aov_96.ma,group_lettering96.ma, by = "genotype" )
colnames(sum_aov_96.ma)[4] <- "group_lettering"
p96.ma <- ggplot(sum_aov_96.ma, aes(x=reorder(genotype, -hsa.miR.96.5p), y=hsa.miR.96.5p, fill=genotype)) +
geom_bar(stat="identity", color="black",
position=position_dodge(),width = 0.5) +
geom_errorbar(aes(ymin=pmax(hsa.miR.96.5p-sd,0), ymax=hsa.miR.96.5p+sd), width=.2,
position=position_dodge(.9)) +
scale_fill_manual("Genotype",values = c("CONTROL"="#374E55FF","USH1B"="#DF8F44FF","USH1D"="#00A1D5FF", "USH2A"="#B24745FF","USH3A"="#79AF97FF"))+
geom_text(aes(label = group_lettering, y = hsa.miR.96.5p + sd), vjust=-0.4, position=position_dodge(0.9))+
labs(title = "miRNA-96-5p") +
theme(plot.title = element_text(hjust = 0.5),
legend.position="none",
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
xlab("") +
ylab("miRNA-96-5p Count")
p96.ma
182-5p Microarray
# Compute the analysis of variance
res.aov182.ma <- aov( hsa.miR.182.5p ~ genotype, data = newdf)
# Summary of the analysis
sum_aov_182.ma <- data_summary(newdf,varname = "hsa.miR.182.5p", groupnames = "genotype")
tukey182.ma <- TukeyHSD(res.aov182.ma)
summary(res.aov182.ma)## Df Sum Sq Mean Sq F value Pr(>F)
## genotype 4 9904 2475.9 15.34 0.000115 ***
## Residuals 12 1936 161.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1group_lettering182.ma <- multcompLetters4(res.aov182.ma,tukey182.ma) #
group_lettering182.ma## $genotype
## CONTROL USH1D USH3A USH1B USH2A
## "a" "b" "b" "b" "b"group_lettering182.ma <- data.frame(group_lettering182.ma$genotype$Letters)
group_lettering182.ma$genotype <- rownames(group_lettering182.ma)
sum_aov_182.ma <- merge(sum_aov_182.ma,group_lettering182.ma, by = "genotype" )
colnames(sum_aov_182.ma)[4] <- "group_lettering"
p182.ma <- ggplot(sum_aov_182.ma, aes(x=reorder(genotype, -hsa.miR.182.5p), y=hsa.miR.182.5p, fill=genotype)) +
geom_bar(stat="identity", color="black",
position=position_dodge(),width = 0.5) +
geom_errorbar(aes(ymin=pmax(hsa.miR.182.5p-sd,0), ymax=hsa.miR.182.5p+sd), width=.2,
position=position_dodge(.9)) +
scale_fill_manual("Genotype",values = c("CONTROL"="#374E55FF","USH1B"="#DF8F44FF","USH1D"="#00A1D5FF", "USH2A"="#B24745FF","USH3A"="#79AF97FF"))+
geom_text(aes(label = group_lettering, y = hsa.miR.182.5p + sd), vjust=-0.4, position=position_dodge(0.9))+
labs(title = "miRNA-182-5p") +
theme(plot.title = element_text(hjust = 0.5),
legend.position="none",
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
xlab("") +
ylab("miRNA-182-5p Count")
p182.ma
183-5p Microarray
# Compute the analysis of variance
res.aov183.ma <- aov( hsa.miR.183.5p ~ genotype, data = newdf)
# Summary of the analysis
sum_aov_183.ma <- data_summary(newdf,varname = "hsa.miR.183.5p", groupnames = "genotype")
tukey183.ma <- TukeyHSD(res.aov183.ma)
summary(res.aov183.ma)## Df Sum Sq Mean Sq F value Pr(>F)
## genotype 4 20327 5082 59.71 0.0000000805 ***
## Residuals 12 1021 85
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1group_lettering183.ma <- multcompLetters4(res.aov183.ma,tukey183.ma) #
group_lettering183.ma## $genotype
## CONTROL USH1D USH3A USH2A USH1B
## "a" "b" "b" "b" "b"group_lettering183.ma <- data.frame(group_lettering183.ma$genotype$Letters)
group_lettering183.ma$genotype <- rownames(group_lettering183.ma)
sum_aov_183.ma <- merge(sum_aov_183.ma,group_lettering183.ma, by = "genotype" )
colnames(sum_aov_183.ma)[4] <- "group_lettering"
p183.ma <- ggplot(sum_aov_183.ma, aes(x=reorder(genotype,-hsa.miR.183.5p), y=hsa.miR.183.5p, fill=genotype)) +
geom_bar(stat="identity", color="black",
position=position_dodge(),width = 0.5) +
geom_errorbar(aes(ymin=pmax(hsa.miR.183.5p-sd,0), ymax=hsa.miR.183.5p+sd), width=.2,
position=position_dodge(.9)) +
scale_fill_manual("Genotype",values = c("CONTROL"="#374E55FF","USH1B"="#DF8F44FF","USH1D"="#00A1D5FF", "USH2A"="#B24745FF","USH3A"="#79AF97FF"))+
geom_text(aes(label = group_lettering, y = hsa.miR.183.5p + sd), vjust=-0.4, position=position_dodge(0.9))+
labs(title = "miRNA-183-5p") +
theme(plot.title = element_text(hjust = 0.5),
legend.position="none",
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
xlab("") +
ylab("miRNA-183-5p Count")
p183.ma
150-5p Microarray
# Compute the analysis of variance
res.aov150.ma <- aov( hsa.miR.150.5p ~ genotype, data = newdf)
# Summary of the analysis
sum_aov_150.ma <- data_summary(newdf,varname = "hsa.miR.150.5p", groupnames = "genotype")
tukey150.ma <- TukeyHSD(res.aov150.ma)
summary(res.aov150.ma)## Df Sum Sq Mean Sq F value Pr(>F)
## genotype 4 5915775 1478944 19.4 0.0000358 ***
## Residuals 12 914864 76239
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1group_lettering150.ma <- multcompLetters4(res.aov150.ma,tukey150.ma) #
group_lettering150.ma## $genotype
## USH2A USH3A USH1D USH1B CONTROL
## "a" "b" "b" "b" "b"group_lettering150.ma <- data.frame(group_lettering150.ma$genotype$Letters)
group_lettering150.ma$genotype <- rownames(group_lettering150.ma)
sum_aov_150.ma <- merge(sum_aov_150.ma,group_lettering150.ma, by = "genotype" )
colnames(sum_aov_150.ma)[4] <- "group_lettering"
p150.ma <- ggplot(sum_aov_150.ma, aes(x=reorder(genotype, +hsa.miR.150.5p), y=hsa.miR.150.5p, fill=genotype)) +
geom_bar(stat="identity", color="black",
position=position_dodge(),width = 0.5) +
geom_errorbar(aes(ymin=pmax(hsa.miR.150.5p-sd,0), ymax=hsa.miR.150.5p+sd), width=.2,
position=position_dodge(.9)) +
scale_fill_manual("Genotype",values = c("CONTROL"="#374E55FF","USH1B"="#DF8F44FF","USH1D"="#00A1D5FF", "USH2A"="#B24745FF","USH3A"="#79AF97FF"))+
geom_text(aes(label = group_lettering, y = hsa.miR.150.5p + sd), vjust=-0.4, position=position_dodge(0.9))+
labs(title = "miRNA-150-5p") +
theme(plot.title = element_text(hjust = 0.5),
legend.position="none",
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
xlab("") +
ylab("miRNA-150-5p Count")
p150.ma
155-5p Microarray
# Compute the analysis of variance
res.aov155.ma <- aov( hsa.miR.155.5p ~ genotype, data = newdf)
# Summary of the analysis
sum_aov_155.ma <- data_summary(newdf,varname = "hsa.miR.155.5p", groupnames = "genotype")
tukey155.ma <- TukeyHSD(res.aov155.ma)
summary(res.aov155.ma)## Df Sum Sq Mean Sq F value Pr(>F)
## genotype 4 633349839 158337460 70.56 0.0000000311 ***
## Residuals 12 26928266 2244022
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1group_lettering155.ma <- multcompLetters4(res.aov155.ma,tukey155.ma) #
group_lettering155.ma## $genotype
## USH1B USH3A USH2A USH1D CONTROL
## "a" "a" "a" "b" "c"group_lettering155.ma <- data.frame(group_lettering155.ma$genotype$Letters)
group_lettering155.ma$genotype <- rownames(group_lettering155.ma)
sum_aov_155.ma <- merge(sum_aov_155.ma,group_lettering155.ma, by = "genotype" )
colnames(sum_aov_155.ma)[4] <- "group_lettering"
p155.ma <- ggplot(sum_aov_155.ma, aes(x=reorder(genotype,+hsa.miR.155.5p), y=hsa.miR.155.5p, fill=genotype)) +
geom_bar(stat="identity", color="black",
position=position_dodge(),width = 0.5) +
geom_errorbar(aes(ymin=pmax(hsa.miR.155.5p-sd,0), ymax=hsa.miR.155.5p+sd), width=.2,
position=position_dodge(.9)) +
scale_fill_manual("Genotype",values = c("CONTROL"="#374E55FF","USH1B"="#DF8F44FF","USH1D"="#00A1D5FF", "USH2A"="#B24745FF","USH3A"="#79AF97FF"))+
geom_text(aes(label = group_lettering, y = hsa.miR.155.5p + sd), vjust=-0.4, position=position_dodge(0.9))+
labs(title = "miRNA-155-5p") +
theme(plot.title = element_text(hjust = 0.5),
legend.position="none",
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
xlab("") +
ylab("miRNA-155-5p Count")
p155.ma
7a-5p Microarray
# Compute the analysis of variance
res.aov7a.ma <- aov( hsa.let.7a.5p ~ genotype, data = newdf)
# Summary of the analysis
sum_aov_7a.ma <- data_summary(newdf,varname = "hsa.let.7a.5p", groupnames = "genotype")
tukey7a.ma <- TukeyHSD(res.aov7a.ma)
summary(res.aov7a.ma)## Df Sum Sq Mean Sq F value Pr(>F)
## genotype 4 43804241 10951060 62.89 0.0000000599 ***
## Residuals 12 2089605 174134
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1group_lettering7a.ma <- multcompLetters4(res.aov7a.ma,tukey7a.ma) #
group_lettering7a.ma## $genotype
## USH2A USH3A USH1B USH1D CONTROL
## "a" "a" "b" "c" "c"group_lettering7a.ma <- data.frame(group_lettering7a.ma$genotype$Letters)
group_lettering7a.ma$genotype <- rownames(group_lettering7a.ma)
sum_aov_7a.ma <- merge(sum_aov_7a.ma,group_lettering7a.ma, by = "genotype" )
colnames(sum_aov_7a.ma)[4] <- "group_lettering"
p7a.ma <- ggplot(sum_aov_7a.ma, aes(x=reorder(genotype, +hsa.let.7a.5p), y=hsa.let.7a.5p, fill=genotype)) +
geom_bar(stat="identity", color="black",
position=position_dodge(),width = 0.5) +
geom_errorbar(aes(ymin=pmax(hsa.let.7a.5p-sd,0), ymax=hsa.let.7a.5p+sd), width=.2,
position=position_dodge(.9)) +
scale_fill_manual("Genotype",values = c("CONTROL"="#374E55FF","USH1B"="#DF8F44FF","USH1D"="#00A1D5FF", "USH2A"="#B24745FF","USH3A"="#79AF97FF"))+
geom_text(aes(label = group_lettering, y = hsa.let.7a.5p + sd), vjust=-0.4, position=position_dodge(0.9))+
labs(title = "let-7a-5p") +
theme(plot.title = element_text(hjust = 0.5),
legend.position="none",
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
xlab("") +
ylab("let-7a-5p Count")
p7a.ma
363-3p Microarray
# Compute the analysis of variance
res.aov363.ma <- aov( hsa.miR.363.3p ~ genotype, data = newdf)
# Summary of the analysis
sum_aov_363.ma <- data_summary(newdf,varname = "hsa.miR.363.3p", groupnames = "genotype")
tukey363.ma <- TukeyHSD(res.aov363.ma)
summary(res.aov363.ma)## Df Sum Sq Mean Sq F value Pr(>F)
## genotype 4 810706 202676 66.1 0.0000000451 ***
## Residuals 12 36792 3066
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1group_lettering363.ma <- multcompLetters4(res.aov363.ma,tukey363.ma) #
group_lettering363.ma## $genotype
## USH1B USH1D USH3A USH2A CONTROL
## "a" "b" "c" "c" "d"group_lettering363.ma <- data.frame(group_lettering363.ma$genotype$Letters)
group_lettering363.ma$genotype <- rownames(group_lettering363.ma)
sum_aov_363.ma <- merge(sum_aov_363.ma,group_lettering363.ma, by = "genotype" )
colnames(sum_aov_363.ma)[4] <- "group_lettering"
p363.ma <- ggplot(sum_aov_363.ma, aes(x=reorder(genotype,+hsa.miR.363.3p), y=hsa.miR.363.3p, fill=genotype)) +
geom_bar(stat="identity", color="black",
position=position_dodge(),width = 0.5) +
geom_errorbar(aes(ymin=pmax(hsa.miR.363.3p-sd,0), ymax=hsa.miR.363.3p+sd), width=.2,
position=position_dodge(.9)) +
scale_fill_manual("Genotype",values = c("CONTROL"="#374E55FF","USH1B"="#DF8F44FF","USH1D"="#00A1D5FF", "USH2A"="#B24745FF","USH3A"="#79AF97FF"))+
geom_text(aes(label = group_lettering, y = hsa.miR.363.3p + sd), vjust=-0.4, position=position_dodge(0.9))+
labs(title = "miRNA-363-3p") +
theme(plot.title = element_text(hjust = 0.5),
legend.position="none",
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
xlab("") +
ylab("miRNA-363-3p Count")
p363.ma
223-3p Microarray
# Compute the analysis of variance
res.aov223.ma <- aov( hsa.miR.223.3p ~ genotype, data = newdf)
# Summary of the analysis
sum_aov_223.ma <- data_summary(newdf,varname = "hsa.miR.223.3p", groupnames = "genotype")
tukey223.ma <- TukeyHSD(res.aov223.ma)
summary(res.aov223.ma)## Df Sum Sq Mean Sq F value Pr(>F)
## genotype 4 344295 86074 84.6 0.000000011 ***
## Residuals 12 12209 1017
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1group_lettering223.ma <- multcompLetters4(res.aov223.ma,tukey223.ma) #
group_lettering223.ma## $genotype
## USH2A USH3A USH1D USH1B CONTROL
## "a" "b" "c" "c" "c"group_lettering223.ma <- data.frame(group_lettering223.ma$genotype$Letters)
group_lettering223.ma$genotype <- rownames(group_lettering223.ma)
sum_aov_223.ma <- merge(sum_aov_223.ma,group_lettering223.ma, by = "genotype" )
colnames(sum_aov_223.ma)[4] <- "group_lettering"
p223.ma <- ggplot(sum_aov_223.ma, aes(x=reorder(genotype,+hsa.miR.223.3p), y=hsa.miR.223.3p, fill=genotype)) +
geom_bar(stat="identity", color="black",
position=position_dodge(),width = 0.5) +
geom_errorbar(aes(ymin=pmax(hsa.miR.223.3p-sd,0), ymax=hsa.miR.223.3p+sd), width=.2,
position=position_dodge(.9)) +
scale_fill_manual("Genotype",values = c("CONTROL"="#374E55FF","USH1B"="#DF8F44FF","USH1D"="#00A1D5FF", "USH2A"="#B24745FF","USH3A"="#79AF97FF"))+
geom_text(aes(label = group_lettering, y = hsa.miR.223.3p + sd), vjust=-0.4, position=position_dodge(0.9))+
labs(title = "miRNA-223-3p") +
theme(plot.title = element_text(hjust = 0.5),
legend.position="none",
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
xlab("") +
ylab("miRNA-223-3p Count")
p223.ma
142-3p Microarray
# Compute the analysis of variance
res.aov142.ma <- aov( hsa.miR.142.3p ~ genotype, data = newdf)
# Summary of the analysis
sum_aov_142.ma <- data_summary(newdf,varname = "hsa.miR.142.3p", groupnames = "genotype")
tukey142.ma <- TukeyHSD(res.aov142.ma)
summary(res.aov142.ma)## Df Sum Sq Mean Sq F value Pr(>F)
## genotype 4 8425580545 2106395136 56.73 0.000000108 ***
## Residuals 12 445562563 37130214
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1group_lettering142.ma <- multcompLetters4(res.aov142.ma,tukey142.ma) #
group_lettering142.ma## $genotype
## USH3A USH2A USH1B USH1D CONTROL
## "a" "a" "b" "bc" "c"group_lettering142.ma <- data.frame(group_lettering142.ma$genotype$Letters)
group_lettering142.ma$genotype <- rownames(group_lettering142.ma)
sum_aov_142.ma <- merge(sum_aov_142.ma,group_lettering142.ma, by = "genotype" )
colnames(sum_aov_142.ma)[4] <- "group_lettering"
p142.ma <- ggplot(sum_aov_142.ma, aes(x=reorder(genotype, +hsa.miR.142.3p), y=hsa.miR.142.3p, fill=genotype)) +
geom_bar(stat="identity", color="black",
position=position_dodge(),width = 0.5) +
geom_errorbar(aes(ymin=pmax(hsa.miR.142.3p-sd,0), ymax=hsa.miR.142.3p+sd), width=.2,
position=position_dodge(.9)) +
scale_fill_manual("Genotype",values = c("CONTROL"="#374E55FF","USH1B"="#DF8F44FF","USH1D"="#00A1D5FF", "USH2A"="#B24745FF","USH3A"="#79AF97FF"))+
geom_text(aes(label = group_lettering, y = hsa.miR.142.3p + sd), vjust=-0.4, position=position_dodge(0.9))+
labs(title = "miRNA-142-3p") +
theme(plot.title = element_text(hjust = 0.5),
legend.position="none",
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
xlab("") +
ylab("miRNA-142-3p Count")
p142.ma
microarray anova combined
down_anovas.ma <- ggarrange(p28.ma,p96.ma,p182.ma,p183.ma,p16.ma,p19b.ma,
nrow = 2,
ncol = 3,
labels = "AUTO",
legend = "none")
down_anovas.ma
#ggsave(down_anovas.ma, filename = "microarray_down.pdf", device = "pdf", width = 10, height = 10)
up_anovas.ma <- ggarrange(p363.ma,p223.ma,p150.ma,p155.ma,p7a.ma,p142.ma,
nrow = 2,
ncol = 3,
labels = "AUTO",
legend = "none")
up_anovas.ma
#ggsave(up_anovas.ma, filename = "microarray_up.pdf", device = "pdf", width = 10, height = 10)